#highest element in two lists python

[gibelwho]

Top 5: Directorial Debuts

This Top 5 reviews Directorial Debuts, considering the film that was the opening gambit in a director’s career. The requirements for this list are straightforward - the films considered must be full length and had a theatrical run, so short films (although oftentimes a way into the business for directors) were not counted and neither were made for TV movies. Additionally, this list is considering films that were directed by a single person, so first films with co-director’s were not considered (although some fantastic films fall into this category, such as On the Town or Monty Python and the Holy Grail). The final consideration, although not a firm requirement, was that this first film was an opening artistic achievement that became a launching point for a notable and long career to follow.

Gibelwho Productions Presents Directorial Debuts:

5. Spike Jonze / Being John Malkovich

4. Rob Reiner / This is Spinal Tap

3. Pete Doctor / Monsters, Inc.

2. Alex Garland / Ex Machina

Rob Marshall / Chicago

Spike Jonze / Being John Malkovich (1999): This film was not only the theatrical directorial debut for music video savant Spike Jonze, but was also the first feature penned by the now acclaimed screenwriter Charlie Kaufman. Their appreciation for bizarre storylines and unique artistic sensibilities combined to make a mark on the cinematic landscape of Hollywood and opened the doors for both to careers that continue to tell non-traditional stories. Jonze had to contend with a confounding script, but managed to keep the audiences engaged with the twists and turns, and also tuned into the emotional journeys of his main players, including a representation of real-life actor John Malkovich himself. The film is filled with memorable visual sequences, including an office floor with cramped ceilings, John Malkovich’s point of view shot when ordering bath towels, and the incredible mind-bending sequence when John Malkovich enters the portal into his own mind, encountering a world filled with multiple John Malkovichs.

Rob Reiner / This is Spinal Tap (1984): Not only is Rob Reiner’s first feature a hilarious mixture of conceits with a generous helping of improvisation from comedic actors, but it also launched an entirely new genre - the mockumentary. To keep the documentary feel, Reiner produced a mix of shooting styles, including hand-held cinema-verite style, titles to introduce band members, creating black and white faux television “archival” footage, traditional documentary interview footage, and also capturing onstage theatrics. While most of the humor is in the actor’s improvised lines, the camera is not just silently observing, but also gets into the jokes and elevates the gags with visual commentary. With this film, Reiner transitioned from an actor to an established director and continued into a fabulous career that dipped into a multitude of different genres, producing several films now considered modern classics.

Pete Doctor / Monsters, Inc. (2001): While Toy Story was the original revolutionary release from the new animation studio Pixar (also with a first time director), Monsters, Inc. earns its place on this list because of the genius of Pete Doctor. The film was the fourth feature from Pixar, and the first to be helmed by a director other than John Lasseter. Pixar’s legacy (and now future, as he has assumed the role of Chief Creative Officer at Pixar following Lasseter’s exit for inappropriate behavior), was in safe hands with Doctor, who has consistently produced the Pixar films with the most unique conceits and beloved characters. This all started with his story development and leadership on Monsters, Inc., a film that achieved technical advancement with the realistic rendering of monster Sulley’s fur, but also one of the most breathtaking action sequences Pixar has ever envisioned, involving the hunt for little Boo’s bedroom door amongst a cavalcade of children’s doors, all swirling around madly in the warehouse storage space. Doctor’s first effort at Pixar produced a delightful tale, proving that Pixar could still herald the magic when the reigns were handed to other directors, and setting him up for more delightful classics to be directed.

Alex Garland / Ex Machina (2014): Alex Garland transitioned from a successful screenwriting career to directing with this astonishing piece of art - intellectual, challenging, visually stunning, and with a twisting plot that ensures the audience is on the edge of their seat throughout the film’s runtime. The screenplay was especially tight, as to be expected from a writer of Garland’s quality, but his work behind the camera was also incredibly solid, playing with the various textures of the setting’s remote mansion’s stone, wood, metal, and glass and also with the robot Ava’s combination of machine metal and human flesh. Garland expertly uncoils a new element in each conversation, scene, and session, slowly expanding the audience's understanding of the world and motivations of each character, until an explosive ending that revels in a woman taking control of her own destiny.

Rob Marshall / Chicago (2002): What are the odds that a directorial debut revitalizes the musical genre for a modern audience - and then goes on to win the Academy Award? Rob Marshall’s background as a dancer and choreographer masterly transferred to the filmmaking space - expertly conceiving the musical numbers (and entire film!), using all the tools at a filmmaker's disposal that a live theatrical experience cannot - camera composition such as close ups, crafting pacing through editing cuts, and matching sound to image. In the best tradition of Bob Fosse, newly minted director Rob Marshall set his mark upon the filmmaking landscape and brought back musicals as a viable avenue for the industry - a popular success at both the box office and amongst critical circles. Plus the film is so damn fun, with inspired performances by Renee Zellweger and Catherine Zeta-Jones as the two murderers on death’s row that just want to make it in show business. Each number tops the next and (with the small exception of Richerd Gere’s tepid singing and dancing skills) are executed with such spirit and razzle dazzle. And all that jazz!

Honorable Mentions:

Orson Welles / Citizen Kane (1941): For the movie that is consistently hailed as the greatest cinematic film ever to be made, it is quite amazing that it was created by a first time director who also cast himself as the lead role. After Orson Welles notorious stunt with the radio broadcast of The War of the Worlds (which caused mayhem as many in the public believed the play was in fact news outlets reporting actual happenings), Hollywood courted this untried director, giving him immense freedom inside the usually structured studio system. The result was a film that experimented with cinematography, editing, writing and narrative structure - all which have since been hailed as innovative leaps forward in the conception and construction of filmmaking. While the film at the time was a box office flop, Welles left his indelible mark on the industry after the promotion of his efforts by the celebrated French film critic and auteur theory supporter Andre Bazan in Cahiers du Cinema. The film can be a bit rough to the tastes of modern audiences, including the exaggerated performance style, but its technical achievements are not to be missed.

Frank Darabont / The Shawshank Redemption (1994): Another film that did middling during its initial box office run, but has since achieved the status of cult and critical favorite, came from the creative spirit of Frank Darabont, who adapted a Stephen King novella and, by sticking to his resolution to lead the film, was eventually given the chance to direct the feature. With a tight screenplay, phenomenal performances by Tim Robbins and Morgan Freeman, and cinematography by the masterful Roger Deakins - it flourished in the rental market and endured to become one of the highest rated films across many critical lists.  

Andrew Niccol / Gattaca (1997): Another writer / new director’s debut contains provocative ideas and powerful imagery paired with striking production design. It is incredible what Andrew Niccol accomplished on a smaller budget, all in service of the story’s dystopian future that feels as though it could be only a few generations away from our own time, where genetics determine a human’s entire future and those who were conceived naturally are condemned to live as a lower caste. The human’s desire to improve their lot in life and explore the universe comes in direct conflict with how science can be used to create fissures in society, enabling human expansion to space, but also limiting a single human’s rights and liberties. Grand ideas and grand design are the drapery for a compelling human story. 

Upcoming

Lin-Manuel Miranda /  tick, tick...Boom! (TBD): Based on the first stage musical by Jonathan Larson, this will be Lin-Manuel Miranda’s first foray into the directorial seat. Since the debut of the smash hit Hamilton, his career has been expanding by leaps and bounds, but he has taken a studied, measured approach to stepping into the director’s role. Miranda cited one reason for signing on to the Mary Poppins Returns movie as a chance to study under the masterful Rob Marshall, receiving a front row seat to a masterclass from one of the best filmmakers to capture musical theater in the cinematic format. Miranda himself was part of a theatrical production of tick, tick...Boom! earlier in his career, so he is very familiar with the material (although the screenplay will need to significantly expand the set of characters from a modest three to include the many roles that have been cast). Unfortunately, at the time of writing, due to the pandemic, filming has shut down; but once they have resumed and the film has a chance to see the light of day on Netflix, I will be eagerly awaiting one of my most beloved musicals to come to life through Miranda’s nascent directorial vision.

[rishiaca]

6 Python Built-in Functions Which You Should Know While Learning Data Science

In this article, we’re going to take a look at some of the Python built-in functions which you should know while learning data science. We’ll be covering the functions Max() and Min(), Len() and Range(), as well as Input() and Print(). By the end of this post, you should have a better understanding of how these functions can be used in data science and what they can do for you.

So, whether you’re a beginner data scientist or you’ve been doing data science for a while, make sure you take a look at these functions and learn how to use them. They’ll be a valuable addition to your data science toolkit.

Max And Min

Data science is a rapidly growing field that is full of mathematical and statistical concepts. However, sometimes it can be difficult to understand these concepts without examples. That's where the max() and min() functions come in handy.

The max and min functions are used to find the highest and lowest values in an iterable. These functions can be used on list, tuple, set, and dictionary data types. Python also has a built-in function called max() which can be used on strings.

Syntax for max() and min() function in Python:

max()

For a single positional argument provided, it's iterable and the largest item in it is returned. Here it is:

max(iterable, *[, key, default])

For two or more arguments provided, the largest value is returned. Here it is:

max(arg1, arg2, *args[, key])

min()

For a single positional argument provided, it's iterable and the smallest item in it is returned. Here it is:

min(iterable, *[, key, default])

For two or more arguments provided, the smallest value is returned. Here it is:

min(arg1, arg2, *args[, key])

The max() and min() functions will return the element with the highest and lowest value, respectively. Next time you're trying to understand a data science concept, don't forget about this helpful Python function.

Len

Len is another important built-in function in Python that can be used to find the length of a list, string, dictionary, or tuple. This function is particularly useful when you need to know how many items are in a collection or when you want to find out how long a particular string or object is.

To use len(), you first need to specify which type of object you are looking for. For example, if you want to find the length of a list, you would use len(list). If you want to know the length of a string, you would use len(string). If you want to find the length of a dictionary, you would use len(dictionary) and so on.

There is one important difference between len() and sum(). sum() returns the total number of items in a collection while len() only returns the length of that particular type of object. For example, if a list has three items and sum(list) is 4, then len(list) will be 3 because sum(list) only counts the number of unique values in list (in this case 3).

Finally, Len can be used in your Python program just like any other function. You can call it using parentheses just like any other command. For example, if we wanted to print out the length of our sample list above we could do something like this: print(The length of our sample list is,len(sample_list))

Range

In data science, it's important to be able to generate sequences of numbers. This is usually done in for loops, where each number in the sequence is generated by running the code one after the other. The range() function is the third important built-in Python function that you should be aware of while learning data science.

The range() function takes three arguments: start, stop, and step; start is the starting number of the sequence, stop is the end number of the sequence, and step is the difference between each number in the sequence. If no argument is given, then start defaults to 0 and stop defaults to infinity, and step defaults to 1. Negative step values can be used to generate a sequence in reverse order (start=-1, stop=0, step=1).

Generally speaking, you'll use range() in for loops to iterate through a sequence of numbers. For example:

for num in range(10): # Generates a list containing 10 numbers

print(num)

Input

Python is a powerful language that is used to write data science algorithms. One of the most important functions that you need to know when learning data science is the input function. The input function takes a single parameter as the prompt string.

The return value of the input function is always a string. Even if the user enters a number, the return value is a string. Therefore, if we want to store the input as an integer, we have to typecast it. We can also specify the datatype which the input function should return by using the eval function. However, be aware that using eval is risky because it can run arbitrary Python code.

Print

When you're learning data science, it's important to know the built-in functions that are available to you. One of these functions is print(), and it's a valuable tool for displaying the output of various data types.

Print can be used to display the output of strings, lists, tuples, dictionaries, sets, and even lists of objects. It can take multiple arguments and can be displayed in a separate line by using a separator (,) as an argument. If you want to display the output without any spaces or newlines, you can use an end argument.

You can also format the output of print() by using a format argument. This argument allows you to specify how the data should be displayed – for example, as text or as a list of values. By formatting your data this way, you make it easier for other people who are working with your data to understand it.

To Sum Things Up

Python is a versatile language that you can use for a variety of tasks. In this blog, we looked at some of the most commonly used functions in Python. We hope you found this information helpful and that you'll be able to put it to good use in your projects.

If you’re aiming to undergo a prolific data science learning journey, Skillslash can help you with its Data Science Course In Bangalore. You’ll be mastering all the theoretical concepts with the help of an industry expert mentor and even receive 1:1 personalized sessions for extra attention. Next, you’ll intern with a top AI startup to gain practical experience by working on 8+ industrial projects from 6+ domains and finally with the help of unlimited job referrals by the Skillslash also has in store, exclusive courses like Full Stack Developer Course in Hyderabad, Web Development Course and Data Structure and Algorithm and System Design Course to ensure aspirants of each domain have a great learning journey and a secure future in these fields. To find out how you can make a career in the IT and tech field with Skillslash, contact the student support team to know more about the course and institute.

[retailgators]

Introduction

We have been frequently said that between two big e-commerce platforms of Malaysia (Shopee and Lazada), one is normally cheaper as well as attracts good deal hunters whereas other usually deals with lesser price sensitive.

So, we have decided to discover ourselves… in the battle of these e-commerce platforms!

For that, we have written a Python script with Selenium as well as Chrome driver for automating the scraping procedure and create a dataset. Here, we would be extracting for these:

Product’s Name

Product’s Name

Then we will do some basic analysis with Pandas on dataset that we have extracted. Here, some data cleaning would be needed and in the end, we will provide price comparisons on an easy visual chart with Seaborn and Matplotlib.

Between these two platforms, we have found Shopee harder to extract data for some reasons: (1) it has frustrating popup boxes that appear while entering the pages; as well as (2) website-class elements are not well-defined (a few elements have different classes).

For the reason, we would start with extracting Lazada first. We will work with Shopee during Part 2!

Initially, we import the required packages:

# Web Scraping from selenium import webdriver from selenium.common.exceptions import * # Data manipulation import pandas as pd # Visualization import matplotlib.pyplot as plt import seaborn as sns

Then, we start the universal variables which are:

Path of a Chrome web driver

Website URL

Items we wish to search

webdriver_path = 'C://Users//me//chromedriver.exe' # Enter the file directory of the Chromedriver Lazada_url = 'https://www.lazada.com.my' search_item = 'Nescafe Gold refill 170g' # Chose this because I often search for coffee!

After that, we would start off the Chrome browser. We would do it with a few customized options:

# Select custom Chrome options options = webdriver.ChromeOptions() options.add_argument('--headless') options.add_argument('start-maximized') options.add_argument('disable-infobars') options.add_argument('--disable-extensions') # Open the Chrome browser browser = webdriver.Chrome(webdriver_path, options=options) browser.get(Lazada_url)

Let’s go through about some alternatives. The ‘— headless’ argument helps you run this script with a browser working in its background. Usually, we would suggest not to add this argument in the Chrome selections, so that you would be able to get the automation as well as recognize bugs very easily. The disadvantage to that is, it’s less effective.

Some other arguments like ‘disable-infobars’, ‘start-maximised’, as well as ‘— disable-extensions’ are included to make sure smoother operations of a browser (extensions, which interfere with the webpages particularly can disrupt the automation procedure).

Running the shorter code block will open your browser.

When the browser gets opened, we would require to automate the item search. The Selenium tool helps you find HTML elements with different techniques including the class, id, CSS selectors, as well as XPath that is the XML path appearance.

Then how do you recognize which features to get? An easy way of doing this is using Chrome’s inspect tool:

search_bar = browser.find_element_by_id('q') search_bar.send_keys(search_item).submit()

That was the easy part. Now a part comes that could be challenging even more in case, you try extract data from Shopee website!

For working out about how you might scrape item names as well as pricing from Lazada, just think about how you might do that manually. What you can? Let’s see:

Copy all the item names as well as their prices onto the spreadsheet table;

Then go to next page as well as repeat the initial step till you’ve got the last page

That’s how we will do that in the automation procedure! To perform that, we will have to get the elements having item names as well as prices with the next page’s button.

With the Chrome’s inspect tool, it’s easy to see that product titles with prices have class names called ‘c16H9d’ as well as ‘c13VH6’ respectively. So, it’s vital to check that the similar class of names applied to all items on a page to make sure successful extraction of all items on a page.

item_titles = browser.find_elements_by_class_name('c16H9d') item_prices = browser.find_elements_by_class_name('c13VH6')

After that, we have unpacked variables like item_titles as well as item_prices in the lists:

# Initialize empty lists titles_list = [] prices_list = [] # Loop over the item_titles and item_prices for title in item_titles:    titles_list.append(title.text) for price in item_prices:    prices_list.append(prices.text)

When we print both the lists, it will show the following outputs:

[‘NESCAFE GOLD Refill 170g x2 packs’, ‘NESCAFE GOLD Original Refill Pack 170g’, ‘Nescafe Gold Refill Pack 170g’, ‘NESCAFE GOLD Refill 170g’, ‘NESCAFE GOLD REFILL 170g’, ‘NESCAFE GOLD Refill 170g’, ‘Nescafe Gold Refill 170g’, ‘[EXPIRY 09/2020] NESCAFE Gold Refill Pack 170g x 2 — NEW PACKAGING!’, ‘NESCAFE GOLD Refill 170g’] [‘RM55.00’, ‘RM22.50’, ‘RM26.76’, ‘RM25.99’, ‘RM21.90’, ‘RM27.50’, ‘RM21.88’, ‘RM27.00’, ‘RM26.76’, ‘RM23.00’, ‘RM46.50’, ‘RM57.30’, ‘RM28.88’]

When we complete scraping from the page, it’s time to move towards the next page. Also, we will utilize a find_element technique using XPath. The use of XPath is very important here as next page buttons have two classes, as well as a find_element_by_class_name technique only gets elements from the single class.

It’ very important that we require to instruct the browser about what to do in case, the subsequent page button gets disabled (means in case, the results are revealed only at one page or in case, we’ve got to the end page results.

try:    browser.find_element_by_xpath(‘//*[@class=”ant-pagination-next” and not(@aria-disabled)]’).click() except NoSuchElementException:    browser.quit()

So, here, we’ve commanded the browser for closing in case the button gets disabled. In case, it’s not got disabled, then the browser will proceed towards the next page as well as we will have to repeat our scraping procedure.

Luckily, the item that we have searched for is having merely 9 items that are displayed on a single page. Therefore, our scraping procedure ends here!

Now, we will start to analyze data that we’ve extracted using Pandas. So, we will start by changing any two lists to the dataframe:

dfL = pd.DataFrame(zip(titles_list, prices_list), columns=[‘ItemName’, ‘Price’])

If the printing of dataframe is done then it shows that our scraping exercise is successful!

When the datasets look good, they aren’t very clean. In case, you print information of a dataframe through Pandas .info() technique it indicates that a Price column category is the string object, instead of the float type. It is very much expected because every entry in a Price column has a currency symbol called ‘RM’ or Malaysian Ringgit. Though, in case the Pricing column is not the float or integer type column, then we won’t be able to scrape any statistical characteristics on that.

`Therefore, we will require to remove that currency symbol as well as convert the whole column into the float type using the following technique:

dfL[‘Price’] = dfL[‘Price’].str.replace(‘RM’, ‘’).astype(float)

Amazing! Although, we need to do some additional cleaning. You could have observed any difference in the datasets. Amongst the items, which is actually the twin pack that we would require to remove from the datasets.

Data cleaning is important for all sorts of data analysis as well as here we would remove entries, which we don’t require with the following code:

# This removes any entry with 'x2' in its title dfL = dfL[dfL[‘ItemName’].str.contains(‘x2’) == False]

Though not required here, you can also make sure that different items, which seem are the items that we precisely searched for. At times other associated products might appear in the search lists, particularly if the search terms aren’t precise enough.

For instance, if we would have searched ‘nescafe gold refill’ rather than ‘nescafe gold refill 170g’, then 117 items might have appeared rather than only 9 that we had scraped earlier. These extra items aren’t some refill packs that we were looking for however, rather capsule filtering cups instead.

Nevertheless, this won’t hurt for filtering your datasets again within the search terms:

dfL = dfL[dfL[‘ItemName’].str.contains(‘170g’) == True]

In the final game, we would also make a column called ‘Platform’ as well as allocate ‘Lazada’ to all the entries here. It is completed so that we could later group different entries by these platforms (Shopee and Lazada) whenever we later organize the pricing comparison between two platforms.

dfL[‘Platform’] = ‘Lazada’

Hurrah! Finally, our dataset is ready and clean!

Now, you need to visualize data with Seaborn and Matplotlib. We would be utilizing the box plot because it exclusively represents the following main statistical features (recognized as a five number summary) in this chart:

Highest Pricing

Lowest Pricing

Median Pricing

25th as well as 75th percentile pricing

# Plot the chart sns.set() _ = sns.boxplot(x=’Platform’, y=’Price’, data=dfL) _ = plt.title(‘Comparison of Nescafe Gold Refill 170g prices between e-commerce platforms in Malaysia’) _ = plt.ylabel(‘Price (RM)’) _ = plt.xlabel(‘E-commerce Platform’) # Show the plot plt.show()

Every box represents the Platform as well as y-axis shows a price range. At this time, we would only get one box, because we haven’t scraped and analyzed any data from a Shopee website.

We could see that item prices range among RM21–28, having the median pricing between RM27–28. Also, we can see that a box has shorter ‘whiskers’, specifying that the pricing is relatively constant without any important outliers. To know more about understanding box plots, just go through this great summary!

That’s it now for this Lazada website! During Part 2, we will go through the particular challenges while extracting the Shopee website as well as we would plot one more box plot used for Shopee pricing to complete the comparison!

Looking to scrape price data from e-commerce websites? Contact Retailgators for eCommerce Data Scraping Services.

source code: https://www.retailgators.com/how-to-scrape-e-commerce-sites-using-web-scraping-to-compare-pricing-using-python.php

[brainitworkstechnology15]

The Ultimate Guide to PCPP Certified Professional in Python Programming 2

PCPP Certified Professional in Python Programming 2 Certification Exam Credential searching for an effective method to grasp Python? Python is acclimated in many fields and may support you land a profitable career probability or excel to your current function. beginning Python classes found on-line are an excellent option as they cater to all skill degrees and budgets.

 There are a whole lot of starting Python courses to make a choice from, so how do you know which are worthwhile? look for these traits when narrowing down your alternatives:

 Python is a fancy programming language and you may need a route that conveys basal knowledge earlier than delving into greater advanced material. This helps dispose of abashing.

 beginning Python classes that consist of added components are most advantageous. which you could gauge your comprehension of the fabric and enforce the talents you’ve discovered right through the instructions. seek lessons with readings, quizzes, actions, tasks or a mixture of each and every.

 steer clear of courses that appoint austere time limits. It’s crucial that you work at a tempo that’s comfy for you and permits you to utterly hold close the fabric taught in the classes.

 We’ve aggregate a list of the superior beginning Python courses on Udemy. Our exact picks are from properly distance gaining knowledge of providers, including Coursera, edX, LinkedIn discovering, crew TreeHouse and Udemy.

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[coresumo]

Future Programming Languages 2025 2030

Which is best Future Programming Languages 2025 2030. When programmers are about to start their coding journey, it is difficult to decide on where to start. Here is a list of the future technology programming languages having a high demand in 2025 and 2030. 

What are the Future Programming Languages 2025 2030 technology having a high demand in 2025 and 2030

Swift 

If you are a mobile developer, Swift is perfect for you! Apple developed it for creating IOS and Mac OS Applications. it remains one of the most in-demand languages of 2021 and will continue to have a high demand in 2025 and also 2030.  Swift is also easy to learn and supports almost everything from objective-C. It is a general-purpose, multi-program compiled programming language. It's Mac-base and if you become good with it, then it's easier to make more money than Android developers. Swift is fast, efficient, secure, enables a high level of interactivity by combining forefront language features. It is a general-purpose programming language built using a modern approach to safety performance and software design patterns. The goal of the swift project is to create the best language for users ranging from systems programming to mobile and also desktop apps scaling up to cloud services. Companies using Swift- Apple, Lyft, Uber.  Python 

Python is undoubtedly a Powerhouse. Its applications extend in many domains like web development, data science, data visualisation, machine learning, artificial intelligence web scraping and also others. It is one of the most popular languages and it is very easy to learn with a vast community and many open source projects. The drawbacks are mainly its slow interpretation since it is a high level language. Python is on top of the job demands and also it has the highest average wages in the tech industry. It is easy to learn. This programming language is great for beginners. It is often use as a scripting language for web applications. Python is the lingua franca of machine learning and also data science. Python's popularity Rose by 3.48% which is very impressive. In Python,  coding are the dynamic type. In coding, you don't need to declare the type of variable. The syntax of python is easy to remember, almost similar to human language. Companies using Python- Instagram, Amazon, Facebook and Spotify.  Java 

Firstly, Java is the leading enterprise programming language at the moment. Java will also be high in demand in 2025 and 2030. It is a general-purpose language use for web pages, and much more and also is the Android dominant language, and it is powerful. It supports distributed computing and multi-threading. And also It is very secure, and it Moves the biggest Enterprises and data centers globally. Today 15 billion devices run Java, and it is being use by 10 million developers worldwide. It is freely accessible and we can run it on all the platforms of the operating systems. Java is best for embedded and also cross-platform applications. Java has a larger number of frameworks and has long lines of code. It is use to develop desktop and mobile applications, big data processing, embedded systems, and so on.  Companies using Java- Uber, Netflix, Instagram, Google Kotlin 

The effortless interoperation between Java and Kotlin Android development is faster and also enjoyable. Scotland addresses the major issues that surfaced in Java,  developers have rewritten several Java apps in Kotlin. The syntax is easy to learn for beginners and also it offers a host of powerful features. It can be a great language to upskill for experienced programmers. It has a Shallow learning curve especially if you have experience in Python or Java. Kotlin is a cross-platform, statically typed, general-purpose programming language with Type inference. It is develop to inter-operate completely with Java. Recently, Google announced that Android development will be increasingly Kotlin- first and that many top apps have already migrated to Kotlin.  Companies using Kotlin- Courser, Uber, Pinterest.  JavaScript

It is the most popular language according to a Stack overflow survey. It is widely know for adding interactive elements to web applications and also browsers. JavaScript is the ultimate language of the web. Almost every web and also mobile application run JavaScript. Since it is a client-side language, many simple applications don't need server support and in the case of complex applications, it produces a server load. There is an insane growth in the usage of this language as well. And also It is also the foundation of most libraries and frameworks for the web surcharge React, Vue and Node. It can run inside nearly all modern web browsers. It is a programming language used primarily by web browsers to create a dynamic and also interactive experience for the users.  Companies using JavaScript- PayPal, Google, Microsoft  Rust 

Rest is a multi-paradigm programming language focused on performance and safety. Rust is syntactically similar to C++. It offers the safety of memory with no use of garbage collection. Rust has great documentation. A friendly compiler with useful error messages and top-notch tooling- an integrated package manager and also build tool. Rust is the language of the future. And also It is the most loved language and one of the highest paying languages in the world. It empowers everyone to build reliable and efficient software. It has the speed and also low-level access of languages like C/C++  with memory security like modern languages. This programming language can run on embedded devices. Rust can easily integrate with other languages. Hundreds of companies Around The World are using rust in production today for fast, low-resource, cross-platform solutions. Companies using Rust- Dropbox, Figma, Discord C++

Firslty, It is a powerful general-purpose programming language. It can develop operating systems, browsers, games, and so on. C++ supports different ways of programming like procedural, object-oriented, functional, and so on. This makes C++ powerful as well as flexible. C++  is old but gold. It is highly use for professional software game development and also high-performance applications. This includes machine learning. It gives programmers a high level of control over the system's resources and memory. We can find this language in today's operating system, graphical user interface, and also embedded systems. It is close to C# and Java;  it makes it easy for programmers to switch to C++ or vice versa. And also was develop as an enhancement of the C language to include an object-orient paradigm.  Companies using C++ - Evernote, Microsoft, Opera, Facebook PHP PHP is a popular general-purpose scripting language that is especially suit for web development. Fast, flexible and pragmatic, PHP powers everything from your blog to the most popular websites in the world. Statistics show that 80% of the top 10 million websites. It creates, reads, opens, deletes, and also closes files on the server. It controls user access and also encrypts data. A wonderful benefit of using PHP is that it can interact with many database languages including my SQL. PHP is free to download and use. And also It is powerful enough to be at the core of the biggest blogging system on the web- WordPress! It is compatible with almost all servers use today like Apache, IIS, and others. It is deep enough to run the largest social network- Facebook. PHP can be easily embed in HTML files and HTML code can also be write in a PHP file.  Companies using PHP- Facebook, Tumblr, Etsy, WordPress C#

C-Sharp is a programming language developed by Microsoft. It runs on the .NET framework. It is use to develop web apps, desktop apps, games, and also much more. Microsoft developed C Sharp as a rival to Java. It is highly use in the enterprise environment and also for game development with the Unity engine. C# gives its free hand to create applications not only for Websites but also for mobile applications. Although it has common points with structure programming languages, it is accept as an object-oriented programming language. There are a massive number of out-of-the-box solutions that you can find in this Programming language but not in other programming languages. For example, tools for unit testing, crypto library, Marvellous collections handling and multi-threading.  Companies using C#- CarMax, RTX, Twitch Scala 

Scala is a programming language that combines Object-oriented programming with functional programming. And also It has a strong static type system and is design to be concise. It operates on the JVM. Also, It is a hybrid of two Programming Paradigms. It tries to address all the criticisms of Java, in which you can keep all the Java libraries and all the advantages of the JVM. At the same time, your code is more concise. Scala is oftentimes use in data science. Scala is a very compatible language and can be very easily install into windows and the Unix operating system easily. This language is useful for developers to enhance their business applications to be more productive, scalable, and reliable. There is no concept of primitive data as everything is an object in Scala. It is design to express the general programming patterns in a refine, succinct, and type-safety way.  Companies Using Scala- Netflix, Sony, Twitter, Linkedin

5 Best Practices for Writing Better Code

Naming conventions  In computer programming, a naming convention is a set of rules for choosing the character sequence to be use for identifiers that denote variables, types, functions, and other entities in source code and documentation.   Three Types of naming conventions are: - Camel case -  Pascal case -  UnderScores Commenting   In computer programming, a comment is a programmer-readable explanation or annotation in the source code for a computer program. We all think our code makes sense, especially if it works but someone else might not to combat this, we all need to get better at source code commenting. Indentation  There are no criteria of following any indentation. The best method is a consistent style. Once you start competing in large projects you will immediately understand the importance of consistent code styling.  Follow DRY principle DRY- Don't Repeat Yourself  It should not repeat the same piece of code over and over again.  How to achieve DRY?   To avoid violating this principle, break your system into pieces. Dissect your code and logic. Break them into smaller reusable units. Don't write lengthy methods. Try to divide the logic and use the existing peace in your method.  Follow KIS principle KIS- Keep It Simple  After all, programming languages are for humans to understand, computers can only understand 0 and 1. So, keep coding simple and straightforward.  How to achieve KIS? To avoid violating this principle, Try to write simple code. Think of many solutions for your problem then choose the best simplest one and transform that into your code.  Whenever programmers face lengthy code, convert it into multiple methods, right-click and reactor in the editor. Try to write small blocks of code that do a single task. Recent Articles: Future Programming Languages 2025 2030 Benefits of Using Angular for Web Development 2021 Difference Between C vs C++ vs Python vs Java KALI Linux Not Prefer Software Development Ubuntu Angular 12 Performance Benchmark Install Features Setup Angular 12 vs 11 vs 10 features benchmark How to Write Business Proposal for Client with Sample Format Top 10 Best Coolest Movies Chris Hemsworth of all time Future Programming Languages 2025 2030 - Writer Taniya Patyal Read the full article

[lyhuynhoanh]

Becoming an Industry Thought Leader: Advanced Techniques for Finding the Best Places to Pitch Guest Posts

Posted by KristinTynski

If you’re involved in any kind of digital PR — or pitching content to writers to expand your brand awareness and build strong links — then you know how hard it can be to find a good home for your content.

I’m about to share the process you can use to identify the best, highest ROI publishers for building consistent, mutually beneficial guest posting relationships with.

This knowledge has been invaluable in understanding which publications have the best reach and authority to other known vertical/niche experts, allowing you to share your own authority within these readership communities.

Before we get started, there’s a caveat: If you aren’t willing to develop true thought leadership, this process won’t work for you. The prerequisite for success here is having a piece of content that is new, newsworthy, and most likely data-driven.

Now let’s get to the good stuff.

Not all publications are equal

Guest posting can increase awareness of your brand, create link authority, and ultimately generate qualified leads. However, that only happens if you pick publishers that have:

The trust of your target audience.

Topical relevance and authority.

Sufficiently large penetration in readership amongst existing authorities in your niche/vertical.

A big trap many fall into is not properly prioritizing their guest posting strategy along these three important metrics.

To put this strategy into context, I’ll provide a detailed methodology for understanding the “thought leadership” space of two different verticals. I’ll also include actionable tips for developing a prioritized list of targets for winning guest spots or columns with your killer content.

It all starts with BuzzSumo

We use BuzzSumo data as the starting point for developing these interactive elements. For this piece, the focus will be on looking at data pulled from their Influencer and Shared Links APIs.

Let’s begin by looking at the data we’re after in the regular user interface. On the Influencers tab, we start by selecting a keyword most representative of the overall niche/industry/vertical we want to understand. We’ll start with “SEO.”

The list of influencers here should already be sorted, but feel free to narrow it down by applying filters. I recommend making sure your final list has 250-500 influencers as a minimum to be comprehensive.

Next, and most importantly, we want to get the links’ shared data for each of these influencers. This will be the data we use to build our network visualizations to truly understand the publishers in the space that are likely to be the highest ROI places for guest posting.

Below you can see the visual readout for one influencer.

Note the distribution of websites Gianluca Fiorelli (@gfiorelli1) most often links to on Twitter. These sites (and their percentages) will be the data we use for our visualization.

Pulling our data programmatically

Thankfully, BuzzSumo has an excellent and intuitive API, so it’s relatively easy to pull and aggregate all of the data we need. I’ve included a link to my script in Github for those who would like to do it themselves.

In general, it does the following:

Generates the first page of influencers for the given keyword, which is about 50. You can either update the script to iterate through pages or just update the page number it pulls from within the script and concatenate the output files after the fact.

For each influencer, it makes another API call and gets all of the aggregated Top Domains shared data for each influencer, which is the same as the data you see in the above pie chart visualization.

Aggregates all the data and exports to a CSV.

Learning from the data

Once we have our data in the format Gephi prefers for network visualizations (sample edge file), we are ready to start exploring. Let’s start with our data from the “SEO” search, for which I pulled the domain sharing data for the top 400 influencers.

A few notes:

The circles are called nodes. All black nodes are the influencer’s Twitter accounts. All other colored nodes are the websites.

The size of the nodes is based on Page Rank. This isn’t the Google Page Rank number, but instead the Page Rank within this graph alone. The larger the node, the more authoritative (and popular) that website is within the entire graph.

The colors of the nodes are based on a modularity algorithm in Gephi. Nodes with similar link graphs typically have the same color.

What can we learn from the SEO influencer graph?

Well, the graph is relatively evenly distributed and cohesive. This indicates that the websites and blogs that are shared most frequently are well known by the entire community.

Additionally, there are a few examples of clusters outside the primary cluster (the middle of the graph). For instance, we see a Local SEO cluster at the 10 p.m. position on the left hand side. We can also see a National Press cluster at the 6-7 p.m. position on the bottom and a French Language cluster at the 1-2 p.m. position at the top right.

Ultimately, Moz, Search Engine Journal, Search Engine Roundtable, Search Engine Land are great bets when developing and fostering guest posting relationships.

Note that part of the complication with this data has to do with publishing volume. The three largest nodes are also some of the most prolific, meaning there are more overall chances for articles to earn Tweets and other social media mentions from industry influencers. You could refining of the data further by normalizing each site by content publishing volume to find publishers who publish much less frequently and still enjoy disproportionate visibility within the industry.

Webmasters.Googleblog.com is a good example of this. They publish 3 to 4 times per month, and yet because of their influence in the industry, they’re still one of the largest and most central nodes. Of course, this makes sense given it is the only public voice of Google for our industry.

Another important thing to notice is the prominence of both YouTube and SlideShare. If you haven’t yet realized the importance and reach of these platforms, perhaps this is the proof you need. Video content and slide decks are highly shared in the SEO community by top influencers.

Differences between SEO and content marketing influencer graphs

What can we learn from the Content Marketing influencer graph?

For starters, it looks somewhat different overall from the SEO influencer graph; it’s much less cohesive and seems to have many more separate clusters. This could indicate that the content publishing sphere for content marketing is perhaps less mature, with more fragmentation and fewer central sources for consuming content marketing related content. It could also be that content marketing is descriptive of more than SEO and that different clusters are publishers that focus more on one type of content marketing vs. another (similar to what we saw with the local SEO cluster in the previous example).

Instead of 3 to 5 similarly sized market leaders, here we see one behemoth, Content Marketing Institute, a testament to both the authority of that brand and the massive amount of content they publish.

We can also see several specific clusters. For instance, the “SEO blogs” cluster in blue at the 8-9 p.m. position and the more general marketing blogs like Hubspot, MarketingProfs, and Social Media Examiner in green and mauve at the 4-5 p.m. position.

The general business top-tier press sites appear quite influential in this space as well, including Forbes, Entrepreneur, Adweek, Tech Crunch, Business Insider, Inc., which we didn’t see as much in the SEO example.

YouTube, again, is extremely important, even more so than in the SEO example.

Is it worth it?

If you’re already deep in an industry, the visualization results of this process are unlikely to shock you. As someone who’s been in the SEO/content marketing industry for 10 years, the graphs are roughly what I expected, but there certainly were some surprises.

This process will be most valuable to you when you are new to an industry or are working within a new vertical or niche. Using the python code I linked and BuzzSumo’s fantastic API and data offers the opportunity to gain a deep visual understanding of the favorite places of industry thought leaders. This knowledge acts as a basis for strategic planning toward identifying top publishers with your own guest content.

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

Becoming an Industry Thought Leader: Advanced Techniques for Finding the Best Places to Pitch Guest Posts Theo dõi các thông tin khác tại: https://foogleseo.blogspot.com Becoming an Industry Thought Leader: Advanced Techniques for Finding the Best Places to Pitch Guest Posts bài đăng bởi foogleseo.blogspot.com from Lý Huỳnh Oanh https://lyhuynhoanh.blogspot.com/2019/12/becoming-industry-thought-leader.html

[chauhuongtran]

Becoming an Industry Thought Leader: Advanced Techniques for Finding the Best Places to Pitch Guest Posts

Posted by KristinTynski

If you’re involved in any kind of digital PR — or pitching content to writers to expand your brand awareness and build strong links — then you know how hard it can be to find a good home for your content.

I’m about to share the process you can use to identify the best, highest ROI publishers for building consistent, mutually beneficial guest posting relationships with.

This knowledge has been invaluable in understanding which publications have the best reach and authority to other known vertical/niche experts, allowing you to share your own authority within these readership communities.

Before we get started, there’s a caveat: If you aren’t willing to develop true thought leadership, this process won’t work for you. The prerequisite for success here is having a piece of content that is new, newsworthy, and most likely data-driven.

Now let’s get to the good stuff.

Not all publications are equal

Guest posting can increase awareness of your brand, create link authority, and ultimately generate qualified leads. However, that only happens if you pick publishers that have:

The trust of your target audience.

Topical relevance and authority.

Sufficiently large penetration in readership amongst existing authorities in your niche/vertical.

A big trap many fall into is not properly prioritizing their guest posting strategy along these three important metrics.

To put this strategy into context, I’ll provide a detailed methodology for understanding the “thought leadership” space of two different verticals. I’ll also include actionable tips for developing a prioritized list of targets for winning guest spots or columns with your killer content.

It all starts with BuzzSumo

We use BuzzSumo data as the starting point for developing these interactive elements. For this piece, the focus will be on looking at data pulled from their Influencer and Shared Links APIs.

Let’s begin by looking at the data we’re after in the regular user interface. On the Influencers tab, we start by selecting a keyword most representative of the overall niche/industry/vertical we want to understand. We’ll start with “SEO.”

The list of influencers here should already be sorted, but feel free to narrow it down by applying filters. I recommend making sure your final list has 250-500 influencers as a minimum to be comprehensive.

Next, and most importantly, we want to get the links’ shared data for each of these influencers. This will be the data we use to build our network visualizations to truly understand the publishers in the space that are likely to be the highest ROI places for guest posting.

Below you can see the visual readout for one influencer.

Note the distribution of websites Gianluca Fiorelli (@gfiorelli1) most often links to on Twitter. These sites (and their percentages) will be the data we use for our visualization.

Pulling our data programmatically

Thankfully, BuzzSumo has an excellent and intuitive API, so it’s relatively easy to pull and aggregate all of the data we need. I’ve included a link to my script in Github for those who would like to do it themselves.

In general, it does the following:

Generates the first page of influencers for the given keyword, which is about 50. You can either update the script to iterate through pages or just update the page number it pulls from within the script and concatenate the output files after the fact.

For each influencer, it makes another API call and gets all of the aggregated Top Domains shared data for each influencer, which is the same as the data you see in the above pie chart visualization.

Aggregates all the data and exports to a CSV.

Learning from the data

Once we have our data in the format Gephi prefers for network visualizations (sample edge file), we are ready to start exploring. Let’s start with our data from the “SEO” search, for which I pulled the domain sharing data for the top 400 influencers.

A few notes:

The circles are called nodes. All black nodes are the influencer’s Twitter accounts. All other colored nodes are the websites.

The size of the nodes is based on Page Rank. This isn’t the Google Page Rank number, but instead the Page Rank within this graph alone. The larger the node, the more authoritative (and popular) that website is within the entire graph.

The colors of the nodes are based on a modularity algorithm in Gephi. Nodes with similar link graphs typically have the same color.

What can we learn from the SEO influencer graph?

Well, the graph is relatively evenly distributed and cohesive. This indicates that the websites and blogs that are shared most frequently are well known by the entire community.

Additionally, there are a few examples of clusters outside the primary cluster (the middle of the graph). For instance, we see a Local SEO cluster at the 10 p.m. position on the left hand side. We can also see a National Press cluster at the 6-7 p.m. position on the bottom and a French Language cluster at the 1-2 p.m. position at the top right.

Ultimately, Moz, Search Engine Journal, Search Engine Roundtable, Search Engine Land are great bets when developing and fostering guest posting relationships.

Note that part of the complication with this data has to do with publishing volume. The three largest nodes are also some of the most prolific, meaning there are more overall chances for articles to earn Tweets and other social media mentions from industry influencers. You could refining of the data further by normalizing each site by content publishing volume to find publishers who publish much less frequently and still enjoy disproportionate visibility within the industry.

Webmasters.Googleblog.com is a good example of this. They publish 3 to 4 times per month, and yet because of their influence in the industry, they’re still one of the largest and most central nodes. Of course, this makes sense given it is the only public voice of Google for our industry.

Another important thing to notice is the prominence of both YouTube and SlideShare. If you haven’t yet realized the importance and reach of these platforms, perhaps this is the proof you need. Video content and slide decks are highly shared in the SEO community by top influencers.

Differences between SEO and content marketing influencer graphs

What can we learn from the Content Marketing influencer graph?

For starters, it looks somewhat different overall from the SEO influencer graph; it’s much less cohesive and seems to have many more separate clusters. This could indicate that the content publishing sphere for content marketing is perhaps less mature, with more fragmentation and fewer central sources for consuming content marketing related content. It could also be that content marketing is descriptive of more than SEO and that different clusters are publishers that focus more on one type of content marketing vs. another (similar to what we saw with the local SEO cluster in the previous example).

Instead of 3 to 5 similarly sized market leaders, here we see one behemoth, Content Marketing Institute, a testament to both the authority of that brand and the massive amount of content they publish.

We can also see several specific clusters. For instance, the “SEO blogs” cluster in blue at the 8-9 p.m. position and the more general marketing blogs like Hubspot, MarketingProfs, and Social Media Examiner in green and mauve at the 4-5 p.m. position.

The general business top-tier press sites appear quite influential in this space as well, including Forbes, Entrepreneur, Adweek, Tech Crunch, Business Insider, Inc., which we didn’t see as much in the SEO example.

YouTube, again, is extremely important, even more so than in the SEO example.

Is it worth it?

If you’re already deep in an industry, the visualization results of this process are unlikely to shock you. As someone who’s been in the SEO/content marketing industry for 10 years, the graphs are roughly what I expected, but there certainly were some surprises.

This process will be most valuable to you when you are new to an industry or are working within a new vertical or niche. Using the python code I linked and BuzzSumo’s fantastic API and data offers the opportunity to gain a deep visual understanding of the favorite places of industry thought leaders. This knowledge acts as a basis for strategic planning toward identifying top publishers with your own guest content.

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

Becoming an Industry Thought Leader: Advanced Techniques for Finding the Best Places to Pitch Guest Posts Theo dõi các thông tin khác tại: https://foogleseo.blogspot.com Becoming an Industry Thought Leader: Advanced Techniques for Finding the Best Places to Pitch Guest Posts posted first on foogleseo.blogspot.com from https://chauhuongtran.blogspot.com/2019/12/becoming-industry-thought-leader.html

[luongthuyvy]

Becoming an Industry Thought Leader: Advanced Techniques for Finding the Best Places to Pitch Guest Posts

Posted by KristinTynski

If you’re involved in any kind of digital PR — or pitching content to writers to expand your brand awareness and build strong links — then you know how hard it can be to find a good home for your content.

I’m about to share the process you can use to identify the best, highest ROI publishers for building consistent, mutually beneficial guest posting relationships with.

This knowledge has been invaluable in understanding which publications have the best reach and authority to other known vertical/niche experts, allowing you to share your own authority within these readership communities.

Before we get started, there’s a caveat: If you aren’t willing to develop true thought leadership, this process won’t work for you. The prerequisite for success here is having a piece of content that is new, newsworthy, and most likely data-driven.

Now let’s get to the good stuff.

Not all publications are equal

Guest posting can increase awareness of your brand, create link authority, and ultimately generate qualified leads. However, that only happens if you pick publishers that have:

The trust of your target audience.

Topical relevance and authority.

Sufficiently large penetration in readership amongst existing authorities in your niche/vertical.

A big trap many fall into is not properly prioritizing their guest posting strategy along these three important metrics.

To put this strategy into context, I’ll provide a detailed methodology for understanding the “thought leadership” space of two different verticals. I’ll also include actionable tips for developing a prioritized list of targets for winning guest spots or columns with your killer content.

It all starts with BuzzSumo

We use BuzzSumo data as the starting point for developing these interactive elements. For this piece, the focus will be on looking at data pulled from their Influencer and Shared Links APIs.

Let’s begin by looking at the data we’re after in the regular user interface. On the Influencers tab, we start by selecting a keyword most representative of the overall niche/industry/vertical we want to understand. We’ll start with “SEO.”

The list of influencers here should already be sorted, but feel free to narrow it down by applying filters. I recommend making sure your final list has 250-500 influencers as a minimum to be comprehensive.

Next, and most importantly, we want to get the links’ shared data for each of these influencers. This will be the data we use to build our network visualizations to truly understand the publishers in the space that are likely to be the highest ROI places for guest posting.

Below you can see the visual readout for one influencer.

Note the distribution of websites Gianluca Fiorelli (@gfiorelli1) most often links to on Twitter. These sites (and their percentages) will be the data we use for our visualization.

Pulling our data programmatically

Thankfully, BuzzSumo has an excellent and intuitive API, so it’s relatively easy to pull and aggregate all of the data we need. I’ve included a link to my script in Github for those who would like to do it themselves.

In general, it does the following:

Generates the first page of influencers for the given keyword, which is about 50. You can either update the script to iterate through pages or just update the page number it pulls from within the script and concatenate the output files after the fact.

For each influencer, it makes another API call and gets all of the aggregated Top Domains shared data for each influencer, which is the same as the data you see in the above pie chart visualization.

Aggregates all the data and exports to a CSV.

Learning from the data

Once we have our data in the format Gephi prefers for network visualizations (sample edge file), we are ready to start exploring. Let’s start with our data from the “SEO” search, for which I pulled the domain sharing data for the top 400 influencers.

A few notes:

The circles are called nodes. All black nodes are the influencer’s Twitter accounts. All other colored nodes are the websites.

The size of the nodes is based on Page Rank. This isn’t the Google Page Rank number, but instead the Page Rank within this graph alone. The larger the node, the more authoritative (and popular) that website is within the entire graph.

The colors of the nodes are based on a modularity algorithm in Gephi. Nodes with similar link graphs typically have the same color.

What can we learn from the SEO influencer graph?

Well, the graph is relatively evenly distributed and cohesive. This indicates that the websites and blogs that are shared most frequently are well known by the entire community.

Additionally, there are a few examples of clusters outside the primary cluster (the middle of the graph). For instance, we see a Local SEO cluster at the 10 p.m. position on the left hand side. We can also see a National Press cluster at the 6-7 p.m. position on the bottom and a French Language cluster at the 1-2 p.m. position at the top right.

Ultimately, Moz, Search Engine Journal, Search Engine Roundtable, Search Engine Land are great bets when developing and fostering guest posting relationships.

Note that part of the complication with this data has to do with publishing volume. The three largest nodes are also some of the most prolific, meaning there are more overall chances for articles to earn Tweets and other social media mentions from industry influencers. You could refining of the data further by normalizing each site by content publishing volume to find publishers who publish much less frequently and still enjoy disproportionate visibility within the industry.

Webmasters.Googleblog.com is a good example of this. They publish 3 to 4 times per month, and yet because of their influence in the industry, they’re still one of the largest and most central nodes. Of course, this makes sense given it is the only public voice of Google for our industry.

Another important thing to notice is the prominence of both YouTube and SlideShare. If you haven’t yet realized the importance and reach of these platforms, perhaps this is the proof you need. Video content and slide decks are highly shared in the SEO community by top influencers.

Differences between SEO and content marketing influencer graphs

What can we learn from the Content Marketing influencer graph?

For starters, it looks somewhat different overall from the SEO influencer graph; it’s much less cohesive and seems to have many more separate clusters. This could indicate that the content publishing sphere for content marketing is perhaps less mature, with more fragmentation and fewer central sources for consuming content marketing related content. It could also be that content marketing is descriptive of more than SEO and that different clusters are publishers that focus more on one type of content marketing vs. another (similar to what we saw with the local SEO cluster in the previous example).

Instead of 3 to 5 similarly sized market leaders, here we see one behemoth, Content Marketing Institute, a testament to both the authority of that brand and the massive amount of content they publish.

We can also see several specific clusters. For instance, the “SEO blogs” cluster in blue at the 8-9 p.m. position and the more general marketing blogs like Hubspot, MarketingProfs, and Social Media Examiner in green and mauve at the 4-5 p.m. position.

The general business top-tier press sites appear quite influential in this space as well, including Forbes, Entrepreneur, Adweek, Tech Crunch, Business Insider, Inc., which we didn’t see as much in the SEO example.

YouTube, again, is extremely important, even more so than in the SEO example.

Is it worth it?

If you’re already deep in an industry, the visualization results of this process are unlikely to shock you. As someone who’s been in the SEO/content marketing industry for 10 years, the graphs are roughly what I expected, but there certainly were some surprises.

This process will be most valuable to you when you are new to an industry or are working within a new vertical or niche. Using the python code I linked and BuzzSumo’s fantastic API and data offers the opportunity to gain a deep visual understanding of the favorite places of industry thought leaders. This knowledge acts as a basis for strategic planning toward identifying top publishers with your own guest content.

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

Becoming an Industry Thought Leader: Advanced Techniques for Finding the Best Places to Pitch Guest Posts Theo dõi các thông tin khác tại: https://foogleseo.blogspot.com Becoming an Industry Thought Leader: Advanced Techniques for Finding the Best Places to Pitch Guest Posts posted first on https://foogleseo.blogspot.com/ #FoogleSEO #luongthuyvy Nguồn: http://bit.ly/2LGSAjM #luongthuyvy

[paulineberry]

Becoming an Industry Thought Leader: Advanced Techniques for Finding the Best Places to Pitch Guest Posts

Posted by KristinTynski

If you’re involved in any kind of digital PR — or pitching content to writers to expand your brand awareness and build strong links — then you know how hard it can be to find a good home for your content.

I’m about to share the process you can use to identify the best, highest ROI publishers for building consistent, mutually beneficial guest posting relationships with.

This knowledge has been invaluable in understanding which publications have the best reach and authority to other known vertical/niche experts, allowing you to share your own authority within these readership communities.

Before we get started, there’s a caveat: If you aren’t willing to develop true thought leadership, this process won’t work for you. The prerequisite for success here is having a piece of content that is new, newsworthy, and most likely data-driven.

Now let’s get to the good stuff.

Not all publications are equal

Guest posting can increase awareness of your brand, create link authority, and ultimately generate qualified leads. However, that only happens if you pick publishers that have:

The trust of your target audience.

Topical relevance and authority.

Sufficiently large penetration in readership amongst existing authorities in your niche/vertical.

A big trap many fall into is not properly prioritizing their guest posting strategy along these three important metrics.

To put this strategy into context, I’ll provide a detailed methodology for understanding the “thought leadership” space of two different verticals. I’ll also include actionable tips for developing a prioritized list of targets for winning guest spots or columns with your killer content.

It all starts with BuzzSumo

We use BuzzSumo data as the starting point for developing these interactive elements. For this piece, the focus will be on looking at data pulled from their Influencer and Shared Links APIs.

Let’s begin by looking at the data we’re after in the regular user interface. On the Influencers tab, we start by selecting a keyword most representative of the overall niche/industry/vertical we want to understand. We’ll start with “SEO.”

The list of influencers here should already be sorted, but feel free to narrow it down by applying filters. I recommend making sure your final list has 250-500 influencers as a minimum to be comprehensive.

Next, and most importantly, we want to get the links’ shared data for each of these influencers. This will be the data we use to build our network visualizations to truly understand the publishers in the space that are likely to be the highest ROI places for guest posting.

Below you can see the visual readout for one influencer.

Note the distribution of websites Gianluca Fiorelli (@gfiorelli1) most often links to on Twitter. These sites (and their percentages) will be the data we use for our visualization.

Pulling our data programmatically

Thankfully, BuzzSumo has an excellent and intuitive API, so it’s relatively easy to pull and aggregate all of the data we need. I’ve included a link to my script in Github for those who would like to do it themselves.

In general, it does the following:

Generates the first page of influencers for the given keyword, which is about 50. You can either update the script to iterate through pages or just update the page number it pulls from within the script and concatenate the output files after the fact.

For each influencer, it makes another API call and gets all of the aggregated Top Domains shared data for each influencer, which is the same as the data you see in the above pie chart visualization.

Aggregates all the data and exports to a CSV.

Learning from the data

Once we have our data in the format Gephi prefers for network visualizations (sample edge file), we are ready to start exploring. Let’s start with our data from the “SEO” search, for which I pulled the domain sharing data for the top 400 influencers.

A few notes:

The circles are called nodes. All black nodes are the influencer’s Twitter accounts. All other colored nodes are the websites.

The size of the nodes is based on Page Rank. This isn’t the Google Page Rank number, but instead the Page Rank within this graph alone. The larger the node, the more authoritative (and popular) that website is within the entire graph.

The colors of the nodes are based on a modularity algorithm in Gephi. Nodes with similar link graphs typically have the same color.

What can we learn from the SEO influencer graph?

Well, the graph is relatively evenly distributed and cohesive. This indicates that the websites and blogs that are shared most frequently are well known by the entire community.

Additionally, there are a few examples of clusters outside the primary cluster (the middle of the graph). For instance, we see a Local SEO cluster at the 10 p.m. position on the left hand side. We can also see a National Press cluster at the 6-7 p.m. position on the bottom and a French Language cluster at the 1-2 p.m. position at the top right.

Ultimately, Moz, Search Engine Journal, Search Engine Roundtable, Search Engine Land are great bets when developing and fostering guest posting relationships.

Note that part of the complication with this data has to do with publishing volume. The three largest nodes are also some of the most prolific, meaning there are more overall chances for articles to earn Tweets and other social media mentions from industry influencers. You could refining of the data further by normalizing each site by content publishing volume to find publishers who publish much less frequently and still enjoy disproportionate visibility within the industry.

Webmasters.Googleblog.com is a good example of this. They publish 3 to 4 times per month, and yet because of their influence in the industry, they’re still one of the largest and most central nodes. Of course, this makes sense given it is the only public voice of Google for our industry.

Another important thing to notice is the prominence of both YouTube and SlideShare. If you haven’t yet realized the importance and reach of these platforms, perhaps this is the proof you need. Video content and slide decks are highly shared in the SEO community by top influencers.

Differences between SEO and content marketing influencer graphs

What can we learn from the Content Marketing influencer graph?

For starters, it looks somewhat different overall from the SEO influencer graph; it’s much less cohesive and seems to have many more separate clusters. This could indicate that the content publishing sphere for content marketing is perhaps less mature, with more fragmentation and fewer central sources for consuming content marketing related content. It could also be that content marketing is descriptive of more than SEO and that different clusters are publishers that focus more on one type of content marketing vs. another (similar to what we saw with the local SEO cluster in the previous example).

Instead of 3 to 5 similarly sized market leaders, here we see one behemoth, Content Marketing Institute, a testament to both the authority of that brand and the massive amount of content they publish.

We can also see several specific clusters. For instance, the “SEO blogs” cluster in blue at the 8-9 p.m. position and the more general marketing blogs like Hubspot, MarketingProfs, and Social Media Examiner in green and mauve at the 4-5 p.m. position.

The general business top-tier press sites appear quite influential in this space as well, including Forbes, Entrepreneur, Adweek, Tech Crunch, Business Insider, Inc., which we didn’t see as much in the SEO example.

YouTube, again, is extremely important, even more so than in the SEO example.

Is it worth it?

If you’re already deep in an industry, the visualization results of this process are unlikely to shock you. As someone who’s been in the SEO/content marketing industry for 10 years, the graphs are roughly what I expected, but there certainly were some surprises.

This process will be most valuable to you when you are new to an industry or are working within a new vertical or niche. Using the python code I linked and BuzzSumo’s fantastic API and data offers the opportunity to gain a deep visual understanding of the favorite places of industry thought leaders. This knowledge acts as a basis for strategic planning toward identifying top publishers with your own guest content.

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

[camerasieunhovn]

Becoming an Industry Thought Leader: Advanced Techniques for Finding the Best Places to Pitch Guest Posts

Posted by KristinTynski

If you’re involved in any kind of digital PR — or pitching content to writers to expand your brand awareness and build strong links — then you know how hard it can be to find a good home for your content.

I’m about to share the process you can use to identify the best, highest ROI publishers for building consistent, mutually beneficial guest posting relationships with.

This knowledge has been invaluable in understanding which publications have the best reach and authority to other known vertical/niche experts, allowing you to share your own authority within these readership communities.

Before we get started, there’s a caveat: If you aren’t willing to develop true thought leadership, this process won’t work for you. The prerequisite for success here is having a piece of content that is new, newsworthy, and most likely data-driven.

Now let’s get to the good stuff.

Not all publications are equal

Guest posting can increase awareness of your brand, create link authority, and ultimately generate qualified leads. However, that only happens if you pick publishers that have:

The trust of your target audience.

Topical relevance and authority.

Sufficiently large penetration in readership amongst existing authorities in your niche/vertical.

A big trap many fall into is not properly prioritizing their guest posting strategy along these three important metrics.

To put this strategy into context, I’ll provide a detailed methodology for understanding the “thought leadership” space of two different verticals. I’ll also include actionable tips for developing a prioritized list of targets for winning guest spots or columns with your killer content.

It all starts with BuzzSumo

We use BuzzSumo data as the starting point for developing these interactive elements. For this piece, the focus will be on looking at data pulled from their Influencer and Shared Links APIs.

Let’s begin by looking at the data we’re after in the regular user interface. On the Influencers tab, we start by selecting a keyword most representative of the overall niche/industry/vertical we want to understand. We’ll start with “SEO.”

The list of influencers here should already be sorted, but feel free to narrow it down by applying filters. I recommend making sure your final list has 250-500 influencers as a minimum to be comprehensive.

Next, and most importantly, we want to get the links’ shared data for each of these influencers. This will be the data we use to build our network visualizations to truly understand the publishers in the space that are likely to be the highest ROI places for guest posting.

Below you can see the visual readout for one influencer.

Note the distribution of websites Gianluca Fiorelli (@gfiorelli1) most often links to on Twitter. These sites (and their percentages) will be the data we use for our visualization.

Pulling our data programmatically

Thankfully, BuzzSumo has an excellent and intuitive API, so it’s relatively easy to pull and aggregate all of the data we need. I’ve included a link to my script in Github for those who would like to do it themselves.

In general, it does the following:

Generates the first page of influencers for the given keyword, which is about 50. You can either update the script to iterate through pages or just update the page number it pulls from within the script and concatenate the output files after the fact.

For each influencer, it makes another API call and gets all of the aggregated Top Domains shared data for each influencer, which is the same as the data you see in the above pie chart visualization.

Aggregates all the data and exports to a CSV.

Learning from the data

Once we have our data in the format Gephi prefers for network visualizations (sample edge file), we are ready to start exploring. Let’s start with our data from the “SEO” search, for which I pulled the domain sharing data for the top 400 influencers.

A few notes:

The circles are called nodes. All black nodes are the influencer’s Twitter accounts. All other colored nodes are the websites.

The size of the nodes is based on Page Rank. This isn’t the Google Page Rank number, but instead the Page Rank within this graph alone. The larger the node, the more authoritative (and popular) that website is within the entire graph.

The colors of the nodes are based on a modularity algorithm in Gephi. Nodes with similar link graphs typically have the same color.

What can we learn from the SEO influencer graph?

Well, the graph is relatively evenly distributed and cohesive. This indicates that the websites and blogs that are shared most frequently are well known by the entire community.

Additionally, there are a few examples of clusters outside the primary cluster (the middle of the graph). For instance, we see a Local SEO cluster at the 10 p.m. position on the left hand side. We can also see a National Press cluster at the 6-7 p.m. position on the bottom and a French Language cluster at the 1-2 p.m. position at the top right.

Ultimately, Moz, Search Engine Journal, Search Engine Roundtable, Search Engine Land are great bets when developing and fostering guest posting relationships.

Note that part of the complication with this data has to do with publishing volume. The three largest nodes are also some of the most prolific, meaning there are more overall chances for articles to earn Tweets and other social media mentions from industry influencers. You could refining of the data further by normalizing each site by content publishing volume to find publishers who publish much less frequently and still enjoy disproportionate visibility within the industry.

Webmasters.Googleblog.com is a good example of this. They publish 3 to 4 times per month, and yet because of their influence in the industry, they’re still one of the largest and most central nodes. Of course, this makes sense given it is the only public voice of Google for our industry.

Another important thing to notice is the prominence of both YouTube and SlideShare. If you haven’t yet realized the importance and reach of these platforms, perhaps this is the proof you need. Video content and slide decks are highly shared in the SEO community by top influencers.

Differences between SEO and content marketing influencer graphs

What can we learn from the Content Marketing influencer graph?

For starters, it looks somewhat different overall from the SEO influencer graph; it’s much less cohesive and seems to have many more separate clusters. This could indicate that the content publishing sphere for content marketing is perhaps less mature, with more fragmentation and fewer central sources for consuming content marketing related content. It could also be that content marketing is descriptive of more than SEO and that different clusters are publishers that focus more on one type of content marketing vs. another (similar to what we saw with the local SEO cluster in the previous example).

Instead of 3 to 5 similarly sized market leaders, here we see one behemoth, Content Marketing Institute, a testament to both the authority of that brand and the massive amount of content they publish.

We can also see several specific clusters. For instance, the “SEO blogs” cluster in blue at the 8-9 p.m. position and the more general marketing blogs like Hubspot, MarketingProfs, and Social Media Examiner in green and mauve at the 4-5 p.m. position.

The general business top-tier press sites appear quite influential in this space as well, including Forbes, Entrepreneur, Adweek, Tech Crunch, Business Insider, Inc., which we didn’t see as much in the SEO example.

YouTube, again, is extremely important, even more so than in the SEO example.

Is it worth it?

If you’re already deep in an industry, the visualization results of this process are unlikely to shock you. As someone who’s been in the SEO/content marketing industry for 10 years, the graphs are roughly what I expected, but there certainly were some surprises.

This process will be most valuable to you when you are new to an industry or are working within a new vertical or niche. Using the python code I linked and BuzzSumo’s fantastic API and data offers the opportunity to gain a deep visual understanding of the favorite places of industry thought leaders. This knowledge acts as a basis for strategic planning toward identifying top publishers with your own guest content.

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

[ductrungnguyen87]

Becoming an Industry Thought Leader: Advanced Techniques for Finding the Best Places to Pitch Guest Posts

Posted by KristinTynski

If you’re involved in any kind of digital PR — or pitching content to writers to expand your brand awareness and build strong links — then you know how hard it can be to find a good home for your content.

I’m about to share the process you can use to identify the best, highest ROI publishers for building consistent, mutually beneficial guest posting relationships with.

This knowledge has been invaluable in understanding which publications have the best reach and authority to other known vertical/niche experts, allowing you to share your own authority within these readership communities.

Before we get started, there’s a caveat: If you aren’t willing to develop true thought leadership, this process won’t work for you. The prerequisite for success here is having a piece of content that is new, newsworthy, and most likely data-driven.

Now let’s get to the good stuff.

Not all publications are equal

Guest posting can increase awareness of your brand, create link authority, and ultimately generate qualified leads. However, that only happens if you pick publishers that have:

The trust of your target audience.

Topical relevance and authority.

Sufficiently large penetration in readership amongst existing authorities in your niche/vertical.

A big trap many fall into is not properly prioritizing their guest posting strategy along these three important metrics.

To put this strategy into context, I’ll provide a detailed methodology for understanding the “thought leadership” space of two different verticals. I’ll also include actionable tips for developing a prioritized list of targets for winning guest spots or columns with your killer content.

It all starts with BuzzSumo

We use BuzzSumo data as the starting point for developing these interactive elements. For this piece, the focus will be on looking at data pulled from their Influencer and Shared Links APIs.

Let’s begin by looking at the data we’re after in the regular user interface. On the Influencers tab, we start by selecting a keyword most representative of the overall niche/industry/vertical we want to understand. We’ll start with “SEO.”

The list of influencers here should already be sorted, but feel free to narrow it down by applying filters. I recommend making sure your final list has 250-500 influencers as a minimum to be comprehensive.

Next, and most importantly, we want to get the links’ shared data for each of these influencers. This will be the data we use to build our network visualizations to truly understand the publishers in the space that are likely to be the highest ROI places for guest posting.

Below you can see the visual readout for one influencer.

Note the distribution of websites Gianluca Fiorelli (@gfiorelli1) most often links to on Twitter. These sites (and their percentages) will be the data we use for our visualization.

Pulling our data programmatically

Thankfully, BuzzSumo has an excellent and intuitive API, so it’s relatively easy to pull and aggregate all of the data we need. I’ve included a link to my script in Github for those who would like to do it themselves.

In general, it does the following:

Generates the first page of influencers for the given keyword, which is about 50. You can either update the script to iterate through pages or just update the page number it pulls from within the script and concatenate the output files after the fact.

For each influencer, it makes another API call and gets all of the aggregated Top Domains shared data for each influencer, which is the same as the data you see in the above pie chart visualization.

Aggregates all the data and exports to a CSV.

Learning from the data

Once we have our data in the format Gephi prefers for network visualizations (sample edge file), we are ready to start exploring. Let’s start with our data from the “SEO” search, for which I pulled the domain sharing data for the top 400 influencers.

A few notes:

The circles are called nodes. All black nodes are the influencer’s Twitter accounts. All other colored nodes are the websites.

The size of the nodes is based on Page Rank. This isn’t the Google Page Rank number, but instead the Page Rank within this graph alone. The larger the node, the more authoritative (and popular) that website is within the entire graph.

The colors of the nodes are based on a modularity algorithm in Gephi. Nodes with similar link graphs typically have the same color.

What can we learn from the SEO influencer graph?

Well, the graph is relatively evenly distributed and cohesive. This indicates that the websites and blogs that are shared most frequently are well known by the entire community.

Additionally, there are a few examples of clusters outside the primary cluster (the middle of the graph). For instance, we see a Local SEO cluster at the 10 p.m. position on the left hand side. We can also see a National Press cluster at the 6-7 p.m. position on the bottom and a French Language cluster at the 1-2 p.m. position at the top right.

Ultimately, Moz, Search Engine Journal, Search Engine Roundtable, Search Engine Land are great bets when developing and fostering guest posting relationships.

Note that part of the complication with this data has to do with publishing volume. The three largest nodes are also some of the most prolific, meaning there are more overall chances for articles to earn Tweets and other social media mentions from industry influencers. You could refining of the data further by normalizing each site by content publishing volume to find publishers who publish much less frequently and still enjoy disproportionate visibility within the industry.

Webmasters.Googleblog.com is a good example of this. They publish 3 to 4 times per month, and yet because of their influence in the industry, they’re still one of the largest and most central nodes. Of course, this makes sense given it is the only public voice of Google for our industry.

Another important thing to notice is the prominence of both YouTube and SlideShare. If you haven’t yet realized the importance and reach of these platforms, perhaps this is the proof you need. Video content and slide decks are highly shared in the SEO community by top influencers.

Differences between SEO and content marketing influencer graphs

What can we learn from the Content Marketing influencer graph?

For starters, it looks somewhat different overall from the SEO influencer graph; it’s much less cohesive and seems to have many more separate clusters. This could indicate that the content publishing sphere for content marketing is perhaps less mature, with more fragmentation and fewer central sources for consuming content marketing related content. It could also be that content marketing is descriptive of more than SEO and that different clusters are publishers that focus more on one type of content marketing vs. another (similar to what we saw with the local SEO cluster in the previous example).

Instead of 3 to 5 similarly sized market leaders, here we see one behemoth, Content Marketing Institute, a testament to both the authority of that brand and the massive amount of content they publish.

We can also see several specific clusters. For instance, the “SEO blogs” cluster in blue at the 8-9 p.m. position and the more general marketing blogs like Hubspot, MarketingProfs, and Social Media Examiner in green and mauve at the 4-5 p.m. position.

The general business top-tier press sites appear quite influential in this space as well, including Forbes, Entrepreneur, Adweek, Tech Crunch, Business Insider, Inc., which we didn’t see as much in the SEO example.

YouTube, again, is extremely important, even more so than in the SEO example.

Is it worth it?

If you’re already deep in an industry, the visualization results of this process are unlikely to shock you. As someone who’s been in the SEO/content marketing industry for 10 years, the graphs are roughly what I expected, but there certainly were some surprises.

This process will be most valuable to you when you are new to an industry or are working within a new vertical or niche. Using the python code I linked and BuzzSumo’s fantastic API and data offers the opportunity to gain a deep visual understanding of the favorite places of industry thought leaders. This knowledge acts as a basis for strategic planning toward identifying top publishers with your own guest content.

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

[isearchgoood]

Becoming an Industry Thought Leader: Advanced Techniques for Finding the Best Places to Pitch Guest Posts

Posted by KristinTynski

If you’re involved in any kind of digital PR — or pitching content to writers to expand your brand awareness and build strong links — then you know how hard it can be to find a good home for your content.

I’m about to share the process you can use to identify the best, highest ROI publishers for building consistent, mutually beneficial guest posting relationships with.

This knowledge has been invaluable in understanding which publications have the best reach and authority to other known vertical/niche experts, allowing you to share your own authority within these readership communities.

Before we get started, there’s a caveat: If you aren’t willing to develop true thought leadership, this process won’t work for you. The prerequisite for success here is having a piece of content that is new, newsworthy, and most likely data-driven.

Now let’s get to the good stuff.

Not all publications are equal

Guest posting can increase awareness of your brand, create link authority, and ultimately generate qualified leads. However, that only happens if you pick publishers that have:

The trust of your target audience.

Topical relevance and authority.

Sufficiently large penetration in readership amongst existing authorities in your niche/vertical.

A big trap many fall into is not properly prioritizing their guest posting strategy along these three important metrics.

To put this strategy into context, I’ll provide a detailed methodology for understanding the “thought leadership” space of two different verticals. I’ll also include actionable tips for developing a prioritized list of targets for winning guest spots or columns with your killer content.

It all starts with BuzzSumo

We use BuzzSumo data as the starting point for developing these interactive elements. For this piece, the focus will be on looking at data pulled from their Influencer and Shared Links APIs.

Let’s begin by looking at the data we’re after in the regular user interface. On the Influencers tab, we start by selecting a keyword most representative of the overall niche/industry/vertical we want to understand. We’ll start with “SEO.”

The list of influencers here should already be sorted, but feel free to narrow it down by applying filters. I recommend making sure your final list has 250-500 influencers as a minimum to be comprehensive.

Next, and most importantly, we want to get the links’ shared data for each of these influencers. This will be the data we use to build our network visualizations to truly understand the publishers in the space that are likely to be the highest ROI places for guest posting.

Below you can see the visual readout for one influencer.

Note the distribution of websites Gianluca Fiorelli (@gfiorelli1) most often links to on Twitter. These sites (and their percentages) will be the data we use for our visualization.

Pulling our data programmatically

Thankfully, BuzzSumo has an excellent and intuitive API, so it’s relatively easy to pull and aggregate all of the data we need. I’ve included a link to my script in Github for those who would like to do it themselves.

In general, it does the following:

Generates the first page of influencers for the given keyword, which is about 50. You can either update the script to iterate through pages or just update the page number it pulls from within the script and concatenate the output files after the fact.

For each influencer, it makes another API call and gets all of the aggregated Top Domains shared data for each influencer, which is the same as the data you see in the above pie chart visualization.

Aggregates all the data and exports to a CSV.

Learning from the data

Once we have our data in the format Gephi prefers for network visualizations (sample edge file), we are ready to start exploring. Let’s start with our data from the “SEO” search, for which I pulled the domain sharing data for the top 400 influencers.

A few notes:

The circles are called nodes. All black nodes are the influencer’s Twitter accounts. All other colored nodes are the websites.

The size of the nodes is based on Page Rank. This isn’t the Google Page Rank number, but instead the Page Rank within this graph alone. The larger the node, the more authoritative (and popular) that website is within the entire graph.

The colors of the nodes are based on a modularity algorithm in Gephi. Nodes with similar link graphs typically have the same color.

What can we learn from the SEO influencer graph?

Well, the graph is relatively evenly distributed and cohesive. This indicates that the websites and blogs that are shared most frequently are well known by the entire community.

Additionally, there are a few examples of clusters outside the primary cluster (the middle of the graph). For instance, we see a Local SEO cluster at the 10 p.m. position on the left hand side. We can also see a National Press cluster at the 6-7 p.m. position on the bottom and a French Language cluster at the 1-2 p.m. position at the top right.

Ultimately, Moz, Search Engine Journal, Search Engine Roundtable, Search Engine Land are great bets when developing and fostering guest posting relationships.

Note that part of the complication with this data has to do with publishing volume. The three largest nodes are also some of the most prolific, meaning there are more overall chances for articles to earn Tweets and other social media mentions from industry influencers. You could refining of the data further by normalizing each site by content publishing volume to find publishers who publish much less frequently and still enjoy disproportionate visibility within the industry.

Webmasters.Googleblog.com is a good example of this. They publish 3 to 4 times per month, and yet because of their influence in the industry, they’re still one of the largest and most central nodes. Of course, this makes sense given it is the only public voice of Google for our industry.

Another important thing to notice is the prominence of both YouTube and SlideShare. If you haven’t yet realized the importance and reach of these platforms, perhaps this is the proof you need. Video content and slide decks are highly shared in the SEO community by top influencers.

Differences between SEO and content marketing influencer graphs

What can we learn from the Content Marketing influencer graph?

For starters, it looks somewhat different overall from the SEO influencer graph; it’s much less cohesive and seems to have many more separate clusters. This could indicate that the content publishing sphere for content marketing is perhaps less mature, with more fragmentation and fewer central sources for consuming content marketing related content. It could also be that content marketing is descriptive of more than SEO and that different clusters are publishers that focus more on one type of content marketing vs. another (similar to what we saw with the local SEO cluster in the previous example).

Instead of 3 to 5 similarly sized market leaders, here we see one behemoth, Content Marketing Institute, a testament to both the authority of that brand and the massive amount of content they publish.

We can also see several specific clusters. For instance, the “SEO blogs” cluster in blue at the 8-9 p.m. position and the more general marketing blogs like Hubspot, MarketingProfs, and Social Media Examiner in green and mauve at the 4-5 p.m. position.

The general business top-tier press sites appear quite influential in this space as well, including Forbes, Entrepreneur, Adweek, Tech Crunch, Business Insider, Inc., which we didn’t see as much in the SEO example.

YouTube, again, is extremely important, even more so than in the SEO example.

Is it worth it?

If you’re already deep in an industry, the visualization results of this process are unlikely to shock you. As someone who’s been in the SEO/content marketing industry for 10 years, the graphs are roughly what I expected, but there certainly were some surprises.

This process will be most valuable to you when you are new to an industry or are working within a new vertical or niche. Using the python code I linked and BuzzSumo’s fantastic API and data offers the opportunity to gain a deep visual understanding of the favorite places of industry thought leaders. This knowledge acts as a basis for strategic planning toward identifying top publishers with your own guest content.

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

via Blogger https://ift.tt/352IgKJ #blogger #bloggingtips #bloggerlife #bloggersgetsocial #ontheblog #writersofinstagram #writingprompt #instapoetry #writerscommunity #writersofig #writersblock #writerlife #writtenword #instawriters #spilledink #wordgasm #creativewriting #poetsofinstagram #blackoutpoetry #poetsofig

[evempierson]

Becoming an Industry Thought Leader: Advanced Techniques for Finding the Best Places to Pitch Guest Posts

Posted by KristinTynski

If you’re involved in any kind of digital PR — or pitching content to writers to expand your brand awareness and build strong links — then you know how hard it can be to find a good home for your content.

I’m about to share the process you can use to identify the best, highest ROI publishers for building consistent, mutually beneficial guest posting relationships with.

This knowledge has been invaluable in understanding which publications have the best reach and authority to other known vertical/niche experts, allowing you to share your own authority within these readership communities.

Before we get started, there’s a caveat: If you aren’t willing to develop true thought leadership, this process won’t work for you. The prerequisite for success here is having a piece of content that is new, newsworthy, and most likely data-driven.

Now let’s get to the good stuff.

Not all publications are equal

Guest posting can increase awareness of your brand, create link authority, and ultimately generate qualified leads. However, that only happens if you pick publishers that have:

The trust of your target audience.

Topical relevance and authority.

Sufficiently large penetration in readership amongst existing authorities in your niche/vertical.

A big trap many fall into is not properly prioritizing their guest posting strategy along these three important metrics.

To put this strategy into context, I’ll provide a detailed methodology for understanding the “thought leadership” space of two different verticals. I’ll also include actionable tips for developing a prioritized list of targets for winning guest spots or columns with your killer content.

It all starts with BuzzSumo

We use BuzzSumo data as the starting point for developing these interactive elements. For this piece, the focus will be on looking at data pulled from their Influencer and Shared Links APIs.

Let’s begin by looking at the data we’re after in the regular user interface. On the Influencers tab, we start by selecting a keyword most representative of the overall niche/industry/vertical we want to understand. We’ll start with “SEO.”

The list of influencers here should already be sorted, but feel free to narrow it down by applying filters. I recommend making sure your final list has 250-500 influencers as a minimum to be comprehensive.

Next, and most importantly, we want to get the links’ shared data for each of these influencers. This will be the data we use to build our network visualizations to truly understand the publishers in the space that are likely to be the highest ROI places for guest posting.

Below you can see the visual readout for one influencer.

Note the distribution of websites Gianluca Fiorelli (@gfiorelli1) most often links to on Twitter. These sites (and their percentages) will be the data we use for our visualization.

Pulling our data programmatically

Thankfully, BuzzSumo has an excellent and intuitive API, so it’s relatively easy to pull and aggregate all of the data we need. I’ve included a link to my script in Github for those who would like to do it themselves.

In general, it does the following:

Generates the first page of influencers for the given keyword, which is about 50. You can either update the script to iterate through pages or just update the page number it pulls from within the script and concatenate the output files after the fact.

For each influencer, it makes another API call and gets all of the aggregated Top Domains shared data for each influencer, which is the same as the data you see in the above pie chart visualization.

Aggregates all the data and exports to a CSV.

Learning from the data

Once we have our data in the format Gephi prefers for network visualizations (sample edge file), we are ready to start exploring. Let’s start with our data from the “SEO” search, for which I pulled the domain sharing data for the top 400 influencers.

A few notes:

The circles are called nodes. All black nodes are the influencer’s Twitter accounts. All other colored nodes are the websites.

The size of the nodes is based on Page Rank. This isn’t the Google Page Rank number, but instead the Page Rank within this graph alone. The larger the node, the more authoritative (and popular) that website is within the entire graph.

The colors of the nodes are based on a modularity algorithm in Gephi. Nodes with similar link graphs typically have the same color.

What can we learn from the SEO influencer graph?

Well, the graph is relatively evenly distributed and cohesive. This indicates that the websites and blogs that are shared most frequently are well known by the entire community.

Additionally, there are a few examples of clusters outside the primary cluster (the middle of the graph). For instance, we see a Local SEO cluster at the 10 p.m. position on the left hand side. We can also see a National Press cluster at the 6-7 p.m. position on the bottom and a French Language cluster at the 1-2 p.m. position at the top right.

Ultimately, Moz, Search Engine Journal, Search Engine Roundtable, Search Engine Land are great bets when developing and fostering guest posting relationships.

Note that part of the complication with this data has to do with publishing volume. The three largest nodes are also some of the most prolific, meaning there are more overall chances for articles to earn Tweets and other social media mentions from industry influencers. You could refining of the data further by normalizing each site by content publishing volume to find publishers who publish much less frequently and still enjoy disproportionate visibility within the industry.

Webmasters.Googleblog.com is a good example of this. They publish 3 to 4 times per month, and yet because of their influence in the industry, they’re still one of the largest and most central nodes. Of course, this makes sense given it is the only public voice of Google for our industry.

Another important thing to notice is the prominence of both YouTube and SlideShare. If you haven’t yet realized the importance and reach of these platforms, perhaps this is the proof you need. Video content and slide decks are highly shared in the SEO community by top influencers.

Differences between SEO and content marketing influencer graphs

What can we learn from the Content Marketing influencer graph?

For starters, it looks somewhat different overall from the SEO influencer graph; it’s much less cohesive and seems to have many more separate clusters. This could indicate that the content publishing sphere for content marketing is perhaps less mature, with more fragmentation and fewer central sources for consuming content marketing related content. It could also be that content marketing is descriptive of more than SEO and that different clusters are publishers that focus more on one type of content marketing vs. another (similar to what we saw with the local SEO cluster in the previous example).

Instead of 3 to 5 similarly sized market leaders, here we see one behemoth, Content Marketing Institute, a testament to both the authority of that brand and the massive amount of content they publish.

We can also see several specific clusters. For instance, the “SEO blogs” cluster in blue at the 8-9 p.m. position and the more general marketing blogs like Hubspot, MarketingProfs, and Social Media Examiner in green and mauve at the 4-5 p.m. position.

The general business top-tier press sites appear quite influential in this space as well, including Forbes, Entrepreneur, Adweek, Tech Crunch, Business Insider, Inc., which we didn’t see as much in the SEO example.

YouTube, again, is extremely important, even more so than in the SEO example.

Is it worth it?

If you’re already deep in an industry, the visualization results of this process are unlikely to shock you. As someone who’s been in the SEO/content marketing industry for 10 years, the graphs are roughly what I expected, but there certainly were some surprises.

This process will be most valuable to you when you are new to an industry or are working within a new vertical or niche. Using the python code I linked and BuzzSumo’s fantastic API and data offers the opportunity to gain a deep visual understanding of the favorite places of industry thought leaders. This knowledge acts as a basis for strategic planning toward identifying top publishers with your own guest content.

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

[buitatphu]

Becoming an Industry Thought Leader: Advanced Techniques for Finding the Best Places to Pitch Guest Posts

Posted by KristinTynski

If you’re involved in any kind of digital PR — or pitching content to writers to expand your brand awareness and build strong links — then you know how hard it can be to find a good home for your content.

I’m about to share the process you can use to identify the best, highest ROI publishers for building consistent, mutually beneficial guest posting relationships with.

This knowledge has been invaluable in understanding which publications have the best reach and authority to other known vertical/niche experts, allowing you to share your own authority within these readership communities.

Before we get started, there’s a caveat: If you aren’t willing to develop true thought leadership, this process won’t work for you. The prerequisite for success here is having a piece of content that is new, newsworthy, and most likely data-driven.

Now let’s get to the good stuff.

Not all publications are equal

Guest posting can increase awareness of your brand, create link authority, and ultimately generate qualified leads. However, that only happens if you pick publishers that have:

The trust of your target audience.

Topical relevance and authority.

Sufficiently large penetration in readership amongst existing authorities in your niche/vertical.

A big trap many fall into is not properly prioritizing their guest posting strategy along these three important metrics.

To put this strategy into context, I’ll provide a detailed methodology for understanding the “thought leadership” space of two different verticals. I’ll also include actionable tips for developing a prioritized list of targets for winning guest spots or columns with your killer content.

It all starts with BuzzSumo

We use BuzzSumo data as the starting point for developing these interactive elements. For this piece, the focus will be on looking at data pulled from their Influencer and Shared Links APIs.

Let’s begin by looking at the data we’re after in the regular user interface. On the Influencers tab, we start by selecting a keyword most representative of the overall niche/industry/vertical we want to understand. We’ll start with “SEO.”

The list of influencers here should already be sorted, but feel free to narrow it down by applying filters. I recommend making sure your final list has 250-500 influencers as a minimum to be comprehensive.

Next, and most importantly, we want to get the links’ shared data for each of these influencers. This will be the data we use to build our network visualizations to truly understand the publishers in the space that are likely to be the highest ROI places for guest posting.

Below you can see the visual readout for one influencer.

Note the distribution of websites Gianluca Fiorelli (@gfiorelli1) most often links to on Twitter. These sites (and their percentages) will be the data we use for our visualization.

Pulling our data programmatically

Thankfully, BuzzSumo has an excellent and intuitive API, so it’s relatively easy to pull and aggregate all of the data we need. I’ve included a link to my script in Github for those who would like to do it themselves.

In general, it does the following:

Generates the first page of influencers for the given keyword, which is about 50. You can either update the script to iterate through pages or just update the page number it pulls from within the script and concatenate the output files after the fact.

For each influencer, it makes another API call and gets all of the aggregated Top Domains shared data for each influencer, which is the same as the data you see in the above pie chart visualization.

Aggregates all the data and exports to a CSV.

Learning from the data

Once we have our data in the format Gephi prefers for network visualizations (sample edge file), we are ready to start exploring. Let’s start with our data from the “SEO” search, for which I pulled the domain sharing data for the top 400 influencers.

A few notes:

The circles are called nodes. All black nodes are the influencer’s Twitter accounts. All other colored nodes are the websites.

The size of the nodes is based on Page Rank. This isn’t the Google Page Rank number, but instead the Page Rank within this graph alone. The larger the node, the more authoritative (and popular) that website is within the entire graph.

The colors of the nodes are based on a modularity algorithm in Gephi. Nodes with similar link graphs typically have the same color.

What can we learn from the SEO influencer graph?

Well, the graph is relatively evenly distributed and cohesive. This indicates that the websites and blogs that are shared most frequently are well known by the entire community.

Additionally, there are a few examples of clusters outside the primary cluster (the middle of the graph). For instance, we see a Local SEO cluster at the 10 p.m. position on the left hand side. We can also see a National Press cluster at the 6-7 p.m. position on the bottom and a French Language cluster at the 1-2 p.m. position at the top right.

Ultimately, Moz, Search Engine Journal, Search Engine Roundtable, Search Engine Land are great bets when developing and fostering guest posting relationships.

Note that part of the complication with this data has to do with publishing volume. The three largest nodes are also some of the most prolific, meaning there are more overall chances for articles to earn Tweets and other social media mentions from industry influencers. You could refining of the data further by normalizing each site by content publishing volume to find publishers who publish much less frequently and still enjoy disproportionate visibility within the industry.

Webmasters.Googleblog.com is a good example of this. They publish 3 to 4 times per month, and yet because of their influence in the industry, they’re still one of the largest and most central nodes. Of course, this makes sense given it is the only public voice of Google for our industry.

Another important thing to notice is the prominence of both YouTube and SlideShare. If you haven’t yet realized the importance and reach of these platforms, perhaps this is the proof you need. Video content and slide decks are highly shared in the SEO community by top influencers.

Differences between SEO and content marketing influencer graphs

What can we learn from the Content Marketing influencer graph?

For starters, it looks somewhat different overall from the SEO influencer graph; it’s much less cohesive and seems to have many more separate clusters. This could indicate that the content publishing sphere for content marketing is perhaps less mature, with more fragmentation and fewer central sources for consuming content marketing related content. It could also be that content marketing is descriptive of more than SEO and that different clusters are publishers that focus more on one type of content marketing vs. another (similar to what we saw with the local SEO cluster in the previous example).

Instead of 3 to 5 similarly sized market leaders, here we see one behemoth, Content Marketing Institute, a testament to both the authority of that brand and the massive amount of content they publish.

We can also see several specific clusters. For instance, the “SEO blogs” cluster in blue at the 8-9 p.m. position and the more general marketing blogs like Hubspot, MarketingProfs, and Social Media Examiner in green and mauve at the 4-5 p.m. position.

The general business top-tier press sites appear quite influential in this space as well, including Forbes, Entrepreneur, Adweek, Tech Crunch, Business Insider, Inc., which we didn’t see as much in the SEO example.

YouTube, again, is extremely important, even more so than in the SEO example.

Is it worth it?

If you’re already deep in an industry, the visualization results of this process are unlikely to shock you. As someone who’s been in the SEO/content marketing industry for 10 years, the graphs are roughly what I expected, but there certainly were some surprises.

This process will be most valuable to you when you are new to an industry or are working within a new vertical or niche. Using the python code I linked and BuzzSumo’s fantastic API and data offers the opportunity to gain a deep visual understanding of the favorite places of industry thought leaders. This knowledge acts as a basis for strategic planning toward identifying top publishers with your own guest content.

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

Becoming an Industry Thought Leader: Advanced Techniques for Finding the Best Places to Pitch Guest Posts Theo dõi các thông tin khác tại: https://foogleseo.blogspot.com Becoming an Industry Thought Leader: Advanced Techniques for Finding the Best Places to Pitch Guest Posts posted first on https://foogleseo.blogspot.com

[noithatotoaz]

Becoming an Industry Thought Leader: Advanced Techniques for Finding the Best Places to Pitch Guest Posts

Posted by KristinTynski

If you’re involved in any kind of digital PR — or pitching content to writers to expand your brand awareness and build strong links — then you know how hard it can be to find a good home for your content.

I’m about to share the process you can use to identify the best, highest ROI publishers for building consistent, mutually beneficial guest posting relationships with.

This knowledge has been invaluable in understanding which publications have the best reach and authority to other known vertical/niche experts, allowing you to share your own authority within these readership communities.

Before we get started, there’s a caveat: If you aren’t willing to develop true thought leadership, this process won’t work for you. The prerequisite for success here is having a piece of content that is new, newsworthy, and most likely data-driven.

Now let’s get to the good stuff.

Not all publications are equal

Guest posting can increase awareness of your brand, create link authority, and ultimately generate qualified leads. However, that only happens if you pick publishers that have:

The trust of your target audience.

Topical relevance and authority.

Sufficiently large penetration in readership amongst existing authorities in your niche/vertical.

A big trap many fall into is not properly prioritizing their guest posting strategy along these three important metrics.

To put this strategy into context, I’ll provide a detailed methodology for understanding the “thought leadership” space of two different verticals. I’ll also include actionable tips for developing a prioritized list of targets for winning guest spots or columns with your killer content.

It all starts with BuzzSumo

We use BuzzSumo data as the starting point for developing these interactive elements. For this piece, the focus will be on looking at data pulled from their Influencer and Shared Links APIs.

Let’s begin by looking at the data we’re after in the regular user interface. On the Influencers tab, we start by selecting a keyword most representative of the overall niche/industry/vertical we want to understand. We’ll start with “SEO.”

The list of influencers here should already be sorted, but feel free to narrow it down by applying filters. I recommend making sure your final list has 250-500 influencers as a minimum to be comprehensive.

Next, and most importantly, we want to get the links’ shared data for each of these influencers. This will be the data we use to build our network visualizations to truly understand the publishers in the space that are likely to be the highest ROI places for guest posting.

Below you can see the visual readout for one influencer.

Note the distribution of websites Gianluca Fiorelli (@gfiorelli1) most often links to on Twitter. These sites (and their percentages) will be the data we use for our visualization.

Pulling our data programmatically

Thankfully, BuzzSumo has an excellent and intuitive API, so it’s relatively easy to pull and aggregate all of the data we need. I’ve included a link to my script in Github for those who would like to do it themselves.

In general, it does the following:

Generates the first page of influencers for the given keyword, which is about 50. You can either update the script to iterate through pages or just update the page number it pulls from within the script and concatenate the output files after the fact.

For each influencer, it makes another API call and gets all of the aggregated Top Domains shared data for each influencer, which is the same as the data you see in the above pie chart visualization.

Aggregates all the data and exports to a CSV.

Learning from the data

Once we have our data in the format Gephi prefers for network visualizations (sample edge file), we are ready to start exploring. Let’s start with our data from the “SEO” search, for which I pulled the domain sharing data for the top 400 influencers.

A few notes:

The circles are called nodes. All black nodes are the influencer’s Twitter accounts. All other colored nodes are the websites.

The size of the nodes is based on Page Rank. This isn’t the Google Page Rank number, but instead the Page Rank within this graph alone. The larger the node, the more authoritative (and popular) that website is within the entire graph.

The colors of the nodes are based on a modularity algorithm in Gephi. Nodes with similar link graphs typically have the same color.

What can we learn from the SEO influencer graph?

Well, the graph is relatively evenly distributed and cohesive. This indicates that the websites and blogs that are shared most frequently are well known by the entire community.

Additionally, there are a few examples of clusters outside the primary cluster (the middle of the graph). For instance, we see a Local SEO cluster at the 10 p.m. position on the left hand side. We can also see a National Press cluster at the 6-7 p.m. position on the bottom and a French Language cluster at the 1-2 p.m. position at the top right.

Ultimately, Moz, Search Engine Journal, Search Engine Roundtable, Search Engine Land are great bets when developing and fostering guest posting relationships.

Note that part of the complication with this data has to do with publishing volume. The three largest nodes are also some of the most prolific, meaning there are more overall chances for articles to earn Tweets and other social media mentions from industry influencers. You could refining of the data further by normalizing each site by content publishing volume to find publishers who publish much less frequently and still enjoy disproportionate visibility within the industry.

Webmasters.Googleblog.com is a good example of this. They publish 3 to 4 times per month, and yet because of their influence in the industry, they’re still one of the largest and most central nodes. Of course, this makes sense given it is the only public voice of Google for our industry.

Another important thing to notice is the prominence of both YouTube and SlideShare. If you haven’t yet realized the importance and reach of these platforms, perhaps this is the proof you need. Video content and slide decks are highly shared in the SEO community by top influencers.

Differences between SEO and content marketing influencer graphs

What can we learn from the Content Marketing influencer graph?

For starters, it looks somewhat different overall from the SEO influencer graph; it’s much less cohesive and seems to have many more separate clusters. This could indicate that the content publishing sphere for content marketing is perhaps less mature, with more fragmentation and fewer central sources for consuming content marketing related content. It could also be that content marketing is descriptive of more than SEO and that different clusters are publishers that focus more on one type of content marketing vs. another (similar to what we saw with the local SEO cluster in the previous example).

Instead of 3 to 5 similarly sized market leaders, here we see one behemoth, Content Marketing Institute, a testament to both the authority of that brand and the massive amount of content they publish.

We can also see several specific clusters. For instance, the “SEO blogs” cluster in blue at the 8-9 p.m. position and the more general marketing blogs like Hubspot, MarketingProfs, and Social Media Examiner in green and mauve at the 4-5 p.m. position.

The general business top-tier press sites appear quite influential in this space as well, including Forbes, Entrepreneur, Adweek, Tech Crunch, Business Insider, Inc., which we didn’t see as much in the SEO example.

YouTube, again, is extremely important, even more so than in the SEO example.

Is it worth it?

If you’re already deep in an industry, the visualization results of this process are unlikely to shock you. As someone who’s been in the SEO/content marketing industry for 10 years, the graphs are roughly what I expected, but there certainly were some surprises.

This process will be most valuable to you when you are new to an industry or are working within a new vertical or niche. Using the python code I linked and BuzzSumo’s fantastic API and data offers the opportunity to gain a deep visual understanding of the favorite places of industry thought leaders. This knowledge acts as a basis for strategic planning toward identifying top publishers with your own guest content.

Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read!

[gamebazu]

Becoming an Industry Thought Leader: Advanced Techniques for Finding the Best Places to Pitch Guest Posts

Posted by KristinTynski

If you’re involved in any kind of digital PR — or pitching content to writers to expand your brand awareness and build strong links — then you know how hard it can be to find a good home for your content.

I’m about to share the process you can use to identify the best, highest ROI publishers for building consistent, mutually beneficial guest posting relationships with.

This knowledge has been invaluable in understanding which publications have the best reach and authority to other known vertical/niche experts, allowing you to share your own authority within these readership communities.

Before we get started, there’s a caveat: If you aren’t willing to develop true thought leadership, this process won’t work for you. The prerequisite for success here is having a piece of content that is new, newsworthy, and most likely data-driven.

Now let’s get to the good stuff.

Not all publications are equal

Guest posting can increase awareness of your brand, create link authority, and ultimately generate qualified leads. However, that only happens if you pick publishers that have:

The trust of your target audience.

Topical relevance and authority.

Sufficiently large penetration in readership amongst existing authorities in your niche/vertical.

A big trap many fall into is not properly prioritizing their guest posting strategy along these three important metrics.

To put this strategy into context, I’ll provide a detailed methodology for understanding the “thought leadership” space of two different verticals. I’ll also include actionable tips for developing a prioritized list of targets for winning guest spots or columns with your killer content.

It all starts with BuzzSumo

We use BuzzSumo data as the starting point for developing these interactive elements. For this piece, the focus will be on looking at data pulled from their Influencer and Shared Links APIs.

Let’s begin by looking at the data we’re after in the regular user interface. On the Influencers tab, we start by selecting a keyword most representative of the overall niche/industry/vertical we want to understand. We’ll start with “SEO.”

The list of influencers here should already be sorted, but feel free to narrow it down by applying filters. I recommend making sure your final list has 250-500 influencers as a minimum to be comprehensive.

Next, and most importantly, we want to get the links’ shared data for each of these influencers. This will be the data we use to build our network visualizations to truly understand the publishers in the space that are likely to be the highest ROI places for guest posting.

Below you can see the visual readout for one influencer.

Note the distribution of websites Gianluca Fiorelli (@gfiorelli1) most often links to on Twitter. These sites (and their percentages) will be the data we use for our visualization.

Pulling our data programmatically

Thankfully, BuzzSumo has an excellent and intuitive API, so it’s relatively easy to pull and aggregate all of the data we need. I’ve included a link to my script in Github for those who would like to do it themselves.

In general, it does the following:

Generates the first page of influencers for the given keyword, which is about 50. You can either update the script to iterate through pages or just update the page number it pulls from within the script and concatenate the output files after the fact.

For each influencer, it makes another API call and gets all of the aggregated Top Domains shared data for each influencer, which is the same as the data you see in the above pie chart visualization.

Aggregates all the data and exports to a CSV.

Learning from the data

Once we have our data in the format Gephi prefers for network visualizations (sample edge file), we are ready to start exploring. Let’s start with our data from the “SEO” search, for which I pulled the domain sharing data for the top 400 influencers.

A few notes:

The circles are called nodes. All black nodes are the influencer’s Twitter accounts. All other colored nodes are the websites.

The size of the nodes is based on Page Rank. This isn’t the Google Page Rank number, but instead the Page Rank within this graph alone. The larger the node, the more authoritative (and popular) that website is within the entire graph.

The colors of the nodes are based on a modularity algorithm in Gephi. Nodes with similar link graphs typically have the same color.

What can we learn from the SEO influencer graph?

Well, the graph is relatively evenly distributed and cohesive. This indicates that the websites and blogs that are shared most frequently are well known by the entire community.

Additionally, there are a few examples of clusters outside the primary cluster (the middle of the graph). For instance, we see a Local SEO cluster at the 10 p.m. position on the left hand side. We can also see a National Press cluster at the 6-7 p.m. position on the bottom and a French Language cluster at the 1-2 p.m. position at the top right.

Ultimately, Moz, Search Engine Journal, Search Engine Roundtable, Search Engine Land are great bets when developing and fostering guest posting relationships.

Note that part of the complication with this data has to do with publishing volume. The three largest nodes are also some of the most prolific, meaning there are more overall chances for articles to earn Tweets and other social media mentions from industry influencers. You could refining of the data further by normalizing each site by content publishing volume to find publishers who publish much less frequently and still enjoy disproportionate visibility within the industry.

Webmasters.Googleblog.com is a good example of this. They publish 3 to 4 times per month, and yet because of their influence in the industry, they’re still one of the largest and most central nodes. Of course, this makes sense given it is the only public voice of Google for our industry.

Another important thing to notice is the prominence of both YouTube and SlideShare. If you haven’t yet realized the importance and reach of these platforms, perhaps this is the proof you need. Video content and slide decks are highly shared in the SEO community by top influencers.

Differences between SEO and content marketing influencer graphs

What can we learn from the Content Marketing influencer graph?

For starters, it looks somewhat different overall from the SEO influencer graph; it’s much less cohesive and seems to have many more separate clusters. This could indicate that the content publishing sphere for content marketing is perhaps less mature, with more fragmentation and fewer central sources for consuming content marketing related content. It could also be that content marketing is descriptive of more than SEO and that different clusters are publishers that focus more on one type of content marketing vs. another (similar to what we saw with the local SEO cluster in the previous example).

Instead of 3 to 5 similarly sized market leaders, here we see one behemoth, Content Marketing Institute, a testament to both the authority of that brand and the massive amount of content they publish.

We can also see several specific clusters. For instance, the “SEO blogs” cluster in blue at the 8-9 p.m. position and the more general marketing blogs like Hubspot, MarketingProfs, and Social Media Examiner in green and mauve at the 4-5 p.m. position.

The general business top-tier press sites appear quite influential in this space as well, including Forbes, Entrepreneur, Adweek, Tech Crunch, Business Insider, Inc., which we didn’t see as much in the SEO example.

YouTube, again, is extremely important, even more so than in the SEO example.

Is it worth it?

If you’re already deep in an industry, the visualization results of this process are unlikely to shock you. As someone who’s been in the SEO/content marketing industry for 10 years, the graphs are roughly what I expected, but there certainly were some surprises.

This process will be most valuable to you when you are new to an industry or are working within a new vertical or niche. Using the python code I linked and BuzzSumo’s fantastic API and data offers the opportunity to gain a deep visual understanding of the favorite places of industry thought leaders. This knowledge acts as a basis for strategic planning toward identifying top publishers with your own guest content.

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