#Wikipedia Data Scraping Services

[iwebscrapingblogs]

Wikipedia Data Scraping Services | Scrape Wikipedia Data

In the digital age, data is the lifeblood of decision-making. Businesses, researchers, and enthusiasts constantly seek reliable sources of information to fuel their insights. Wikipedia, with its vast repository of knowledge, serves as a goldmine for this purpose. However, manually extracting data from Wikipedia can be a daunting and time-consuming task. This is where Wikipedia data scraping services come into play, offering a streamlined and efficient way to harness the wealth of information available on the platform.

What is Wikipedia Data Scraping?

Wikipedia data scraping involves using automated tools and techniques to extract information from Wikipedia pages. This process bypasses the need for manual copying and pasting, allowing for the efficient collection of large datasets. Scraping can include extracting text, infoboxes, references, categories, and even multimedia content. The scraped data can then be used for various purposes, such as research, analysis, and integration into other applications.

Why Scrape Wikipedia Data?

Extensive Knowledge Base: Wikipedia hosts millions of articles on a wide range of topics, making it an invaluable resource for information.

Regular Updates: Wikipedia is continuously updated by contributors worldwide, ensuring that the information is current and reliable.

Structured Data: Many Wikipedia pages contain structured data in the form of infoboxes and tables, which can be particularly useful for data analysis.

Open Access: Wikipedia's content is freely accessible, making it a cost-effective source of data for various applications.

Applications of Wikipedia Data Scraping

Academic Research: Researchers can use scraped Wikipedia data to support their studies, gather historical data, or analyze trends over time.

Business Intelligence: Companies can leverage Wikipedia data to gain insights into market trends, competitors, and industry developments.

Machine Learning: Wikipedia's vast dataset can be used to train machine learning models, improve natural language processing algorithms, and develop AI applications.

Content Creation: Writers and content creators can use Wikipedia data to enrich their articles, blogs, and other forms of content.

How Wikipedia Data Scraping Works

Wikipedia data scraping involves several steps:

Identify the Target Pages: Determine which Wikipedia pages or categories contain the data you need.

Select a Scraping Tool: Choose a suitable web scraping tool or service. Popular options include Python libraries like BeautifulSoup and Scrapy, as well as online scraping services.

Develop the Scraping Script: Write a script that navigates to the target pages, extracts the desired data, and stores it in a structured format (e.g., CSV, JSON).

Handle Potential Challenges: Address challenges such as rate limiting, CAPTCHA verification, and dynamic content loading.

Data Cleaning and Processing: Clean and process the scraped data to ensure it is accurate and usable.

Ethical Considerations and Legal Compliance

While Wikipedia data scraping can be incredibly useful, it is essential to approach it ethically and legally. Here are some guidelines to follow:

Respect Wikipedia’s Terms of Service: Ensure that your scraping activities comply with Wikipedia’s terms of use and guidelines.

Avoid Overloading Servers: Implement rate limiting to prevent overwhelming Wikipedia’s servers with too many requests in a short period.

Credit the Source: Always credit Wikipedia as the source of the data and provide links to the original pages where possible.

Privacy Concerns: Be mindful of any personal information that might be present in the scraped data and handle it responsibly.

Choosing the Right Wikipedia Data Scraping Service

Several factors should be considered when selecting a Wikipedia data scraping service:

Reputation: Choose a service with a proven track record and positive reviews from users.

Customization: Look for services that offer customizable scraping solutions tailored to your specific needs.

Data Quality: Ensure the service provides clean, accurate, and well-structured data.

Support and Maintenance: Opt for services that offer ongoing support and maintenance to address any issues that may arise.

Conclusion

Wikipedia data scraping services open up a world of possibilities for accessing and utilizing the vast amounts of information available on the platform. Whether for academic research, business intelligence, machine learning, or content creation, these services provide a powerful tool for extracting valuable insights. By adhering to ethical practices and legal guidelines, users can harness the full potential of Wikipedia data to drive innovation and informed decision-making.

As the demand for data-driven insights continues to grow, Wikipedia data scraping services will undoubtedly play a crucial role in shaping the future of information access and analysis.

[anniekoh]

elsewhere on the internet: AI and advertising

Bubble Trouble (about AIs trained on AI output and the impending model collapse) (Ed Zitron, Mar 2024)

A Wall Street Journal piece from this week has sounded the alarm that some believe AI models will run out of "high-quality text-based data" within the next two years in what an AI researcher called "a frontier research problem."  Modern AI models are trained by feeding them "publicly-available" text from the internet, scraped from billions of websites (everything from Wikipedia to Tumblr, to Reddit), which the model then uses to discern patterns and, in turn, answer questions based on the probability of an answer being correct. Theoretically, the more training data that these models receive, the more accurate their responses will be, or at least that's what the major AI companies would have you believe. Yet AI researcher Pablo Villalobos told the Journal that he believes that GPT-5 (OpenAI's next model) will require at least five times the training data of GPT-4. In layman's terms, these machines require tons of information to discern what the "right" answer to a prompt is, and "rightness" can only be derived from seeing lots of examples of what "right" looks like. ... One (very) funny idea posed by the Journal's piece is that AI companies are creating their own "synthetic" data to train their models, a "computer-science version of inbreeding" that Jathan Sadowski calls Habsburg AI.  This is, of course, a terrible idea. A research paper from last year found that feeding model-generated data to models creates "model collapse" — a "degenerative learning process where models start forgetting improbable events over time as the model becomes poisoned with its own projection of reality."

...

The AI boom has driven global stock markets to their best first quarter in 5 years, yet I fear that said boom is driven by a terrifyingly specious and unstable hype cycle. The companies benefitting from AI aren't the ones integrating it or even selling it, but those powering the means to use it — and while "demand" is allegedly up for cloud-based AI services, every major cloud provider is building out massive data center efforts to capture further demand for a technology yet to prove its necessity, all while saying that AI isn't actually contributing much revenue at all. Amazon is spending nearly $150 billion in the next 15 years on data centers to, and I quote Bloomberg, "handle an expected explosion in demand for artificial intelligence applications" as it tells its salespeople to temper their expectations of what AI can actually do.  I feel like a crazy person every time I read glossy pieces about AI "shaking up" industries only for the substance of the story to be "we use a coding copilot and our HR team uses it to generate emails." I feel like I'm going insane when I read about the billions of dollars being sunk into data centers, or another headline about how AI will change everything that is mostly made up of the reporter guessing what it could do.

They're Looting the Internet (Ed Zitron, Apr 2024)

An investigation from late last year found that a third of advertisements on Facebook Marketplace in the UK were scams, and earlier in the year UK financial services authorities said it had banned more than 10,000 illegal investment ads across Instagram, Facebook, YouTube and TikTok in 2022 — a 1,500% increase over the previous year. Last week, Meta revealed that Instagram made an astonishing $32.4 billion in advertising revenue in 2021. That figure becomes even more shocking when you consider Google's YouTube made $28.8 billion in the same period . Even the giants haven’t resisted the temptation to screw their users. CNN, one of the most influential news publications in the world, hosts both its own journalism and spammy content from "chum box" companies that make hundreds of millions of dollars driving clicks to everything from scams to outright disinformation. And you'll find them on CNN, NBC and other major news outlets, which by proxy endorse stories like "2 Steps To Tell When A Slot Is Close To Hitting The Jackpot."  These “chum box” companies are ubiquitous because they pay well, making them an attractive proposition for cash-strapped media entities that have seen their fortunes decline as print revenues evaporated. But they’re just so incredibly awful. In 2018, the (late, great) podcast Reply All had an episode that centered around a widower whose wife’s death had been hijacked by one of these chum box advertisers to push content that, using stolen family photos, heavily implied she had been unfaithful to him. The title of the episode — An Ad for the Worst Day of your Life — was fitting, and it was only until a massively popular podcast intervened did these networks ban the advert.  These networks are harmful to the user experience, and they’re arguably harmful to the news brands that host them. If I was working for a major news company, I’d be humiliated to see my work juxtaposed with specious celebrity bilge, diet scams, and get-rich-quick schemes.

...

While OpenAI, Google and Meta would like to claim that these are "publicly-available" works that they are "training on," the actual word for what they're doing is "stealing." These models are not "learning" or, let's be honest, "training" on this data, because that's not how they work — they're using mathematics to plagiarize it based on the likelihood that somebody else's answer is the correct one. If we did this as a human being — authoritatively quoting somebody else's figures without quoting them — this would be considered plagiarism, especially if we represented the information as our own. Generative AI allows you to generate lots of stuff from a prompt, allowing you to pretend to do the research much like LLMs pretend to know stuff. It's good for cheating at papers, or generating lots of mediocre stuff LLMs also tend to hallucinate, a virtually-unsolvable problem where they authoritatively make incorrect statements that creates horrifying results in generative art and renders them too unreliable for any kind of mission critical work. Like I’ve said previously, this is a feature, not a bug. These models don’t know anything — they’re guessing, based on mathematical calculations, as to the right answer. And that means they’ll present something that feels right, even though it has no basis in reality. LLMs are the poster child for Stephen Colbert’s concept of truthiness.

[snickerdoodlles]

Don’t you feel that if you go on the “loves ao3 more than their own mom” website aka the “can't throw a rock without hitting a writer” website and put in the main tag the opinion that AI writing models are no big deal and don’t do any harm and nobody has any reason to feel hurt over their unethical training in a tone that distinctly resembles the “toughen up snowflake” rhetoric, you might, in fact, just be the one out of line and also an asshole?

*rolls eyes* not what i did nor what i said.

there are many concerns to have with generative AI, such as the ability to extract privatized information from their training datasets, the exploitation of human workers for AI services (tho this one frankly goes for all internet services, not just AI), AI's reinforced biases and lack of learning, and the current lack of regulation against AI developers and AI usage to name a few. in terms of a direct impact on the creative industry, there are several concerns about the uncompensated and unregulated use of copyrighted materials in training data (paper discussing BookCorpus, courtlistener link for writers suing over Books2), the even worse image scraping for diffusion models, screen production companies trying to pressure people into selling their personal image rights for AI use, and publishers getting slammed with various AI generated content while the copyright laws for it are still massively in flux.

i said fanfic does not intersect with AI. actually, i vaguely whined about it in the tags of an untagged post, because i'm allowed to do that on my personal whine-into-the-void space. (which, tumblr is bad about filtering properly in tags and i'm sorry if it popped up anyways, but i also can't control tumblr search not functioning properly.)

there are concerns to be had about AI training datasets (developers refusing to remove or protect private information because it weakens the training data even tho this is a bigger issue for bigger models is my primary concern personally, but the book shadow libraries and mass image scraping are shady ass shit too). but AO3 was never used to train AI. there is a lot of sketchiness involved with AI training data, but AO3 is not one of them.

i get irked when people compare AI generated writing to AI generated art, because the technology behind it is different. to make art, AI has to directly use the source image to create the final output. this is why people can reverse the process on AI art models to extract the source images. written models (LLMs) learn how to string words into sentences and in terms of remembering the specific training data, LLMs actually have a known issue of wandering attention for general written training material like books/articles/etc. (re the writers' lawsuit -- we know AI developers are pulling shady shit with their use of books, AI developers know they're pulling shady shit with their use of books, but unfortunately the specific proof the writers' are using for their case very closely resembles the summaries and written reviews on their books' wikipedia pages. the burden of proof for copyright violation is really hard to prove for books, esp because copyright protects the expression of an idea, not the idea or individual sentences of a work, and LLMs do not retain their written training material in that way.) these are different issues that can't truly be conflated because the methods in which the materials are used and the potential regulation/the impact of potential regulation on them are different.

anyways, back to my annoyances with fanfic x AI -- fanfic is not involved in its development, and if you don't want to read fic made by AI, don't click on fic that involves AI. ultimately, if you read a fic and it turns out AI was involved...nothing happens. if you don't like it, you click a back button, delete a bookmark, and/or mute a user. AI just strings words together. that's it. acting like AI will have a great impact on fandom, or that fandom will be some final bastion against it, is really fucking annoying to me because fandom does not have any stakes in this. there are legitimate issues in regards to developing and regulating AI (link to the US senate hearing again, because there are so many), but "oh no, what if i read a fic written by AI" is a rather tone fucking deaf complaint, don't you think?

[drmikewatts]

Weekly Review 25 April 2025

Some interesting links that I Tweeted about in the last week (I also post these on Mastodon, Threads, Newsmast, and Bluesky):

How AI can help with taxes and financial planning: https://www.bigdatawire.com/2025/04/18/the-transformative-role-of-ai-in-financial-planning-and-tax-preparation/

Actors who sold their likenesses for AI avatars are regretting doing so: https://arstechnica.com/ai/2025/04/regrets-actors-who-sold-ai-avatars-stuck-in-black-mirror-esque-dystopia/

AI is widely used in the New Zealand public sector, mostly because it helps them with their work: https://www.rnz.co.nz/news/political/558081/ai-widely-used-in-public-sector-survey-finds

One-bit neural network weights, to reduce the memory footprints of large AI: https://arstechnica.com/ai/2025/04/microsoft-researchers-create-super%e2%80%91efficient-ai-that-uses-up-to-96-less-energy/

The role of Chief AI Officer is expected to become more prominent in coming years: https://www.informationweek.com/machine-learning-ai/how-will-the-role-of-chief-ai-officer-evolve-in-2025-

How AI is affecting the creative professions: https://www.nzherald.co.nz/nz/how-ais-latest-advancements-could-reshape-creative-professions-the-front-page/NVIPGTLUR5GVFDBUN4FWJW23EE/

AI agent hallucinates new policy, upsets users to the point of cancelling subscriptions: https://arstechnica.com/ai/2025/04/cursor-ai-support-bot-invents-fake-policy-and-triggers-user-uproar/

AI models get more advanced, but they just hallucinate more: https://techcrunch.com/2025/04/18/openais-new-reasoning-ai-models-hallucinate-more/

Wikipedia got so hammered by AI training bots scraping the site, they have released a data set optimised for training AI to Kaggle: https://www.theverge.com/news/650467/wikipedia-kaggle-partnership-ai-dataset-machine-learning

Google is using AI to block scammers' ads: https://arstechnica.com/gadgets/2025/04/google-used-ai-to-block-three-times-more-fraudulent-advertisers-in-2024/

A New Zealand charity is using AI chatbots to help veterans: https://www.rnz.co.nz/news/national/558481/charity-launches-ai-chatbot-therapy-service-for-veterans

Organisations need to move strategically when rolling out AI, rather than rushing into it: https://www.informationweek.com/machine-learning-ai/the-ai-fomo-trap-build-guardrails-for-the-gold-rush

AI hardware is using so much power that its driving advances in cooling technology for data centres: https://www.techrepublic.com/article/news-data-centers-power-cooling-ai/

Google's AI is learning to speak dolphin: https://www.extremetech.com/science/new-google-llm-aims-to-translate-dolphin-language

Microsoft's Copilot AI is getting quite annoying: https://www.theregister.com/2025/04/18/microsoft_copilot_not_wanted/

A new approach to combat prompt injection attacks against generative AI: https://arstechnica.com/information-technology/2025/04/researchers-claim-breakthrough-in-fight-against-ais-frustrating-security-hole/

The key to employing AI effectively in marketing is to carefully train the model: https://dataconomy.com/2025/04/17/training-your-ai-not-just-your-team-a-marketers-guide-to-smarter-campaigns/

The idea of 'adjusting' - read reducing - AI guardrails because a competitor has done so is rather frightening: https://techcrunch.com/2025/04/15/openai-says-it-may-adjust-its-safety-requirements-if-a-rival-lab-releases-high-risk-ai/

Seeding AI with misinformation is being used more and more by bad actors: https://www.stuff.co.nz/world-news/360659194/russia-seeds-chatbots-lies-and-any-bad-actor-could-game-ai-same-way

While greater efficiencies in AI can allow more to be done for the same amount of energy, the demand is not necessarily there to do so: https://www.economist.com/finance-and-economics/2025/01/30/tech-tycoons-have-got-the-economics-of-ai-wrong

I wouldn't describe it as 'thinking with pictures' as AI don't really 'think': https://www.computerworld.com/article/3964968/open-ais-new-models-can-think-with-pictures.html

AI companies are fine with stealing other people's work, but hate it when someone does the same to them: https://www.theregister.com/2025/04/14/miyazaki_ai_and_intellectual_property/

Could the use of screenshots help AI learn what is important to users? https://www.theverge.com/ai-artificial-intelligence/650809/screenshots-apps-ai-pixel-nothing

A framework for constructing voice AI agents: https://www.kdnuggets.com/the-easiest-way-to-create-real-time-ai-voice-agents

[webdatacrawler0]

What Are the Best Practices to Scrape Wikipedia With Python Efficiently?

Introduction

Wikipedia, the treasure trove of knowledge, is a go-to source for data across various fields, from research and education to business intelligence and content creation. Leveraging this wealth of information can provide a significant advantage for businesses and developers. However, manually collecting data from Wikipedia can be time- consuming and prone to errors. This is where you can Scrape Wikipedia With Python, an efficient, scalable, and reliable method for extracting information.

This blog will explore best practices for web scraping Wikipedia using Python, covering essential tools, ethical considerations, and real-world applications. We’ll also include industry statistics for 2025, examples, and a case study to demonstrate the power of Wikipedia Data Extraction.

Why Scrape Wikipedia With Python?

Wikipedia is one of the largest repositories of knowledge on the internet, providing a vast array of information on diverse topics. For businesses, researchers, and developers, accessing this data efficiently is crucial for making informed decisions, building innovative solutions, and conducting in-depth analyses. Here’s why you should consider Scrape Wikipedia With Python as your go-to approach for data extraction.

Efficiency and Flexibility

Web scraping Wikipedia using Python allows quick and efficient structured and unstructured data extraction. Python’s powerful libraries, like BeautifulSoup, Requests, and Pandas, simplify the process of extracting and organizing data from Wikipedia pages. Unlike manual methods, automation significantly reduces time and effort.

Access to Rich Data

From tables and infoboxes to article content and references, Wikipedia Data Extraction provides a goldmine of information for industries like education, market research, and artificial intelligence. Python’s versatility ensures you can extract exactly what you need, tailored to your use case.

Cost-Effective Solution

Leveraging Web scraping Wikipedia eliminates the need for expensive third-party services. Python scripts allow you to collect data at minimal costs, enhancing scalability and sustainability.

Applications Across Industries

Researchers use Wikipedia Data Extraction to build datasets in natural language processing and knowledge graphs.

Businesses analyze trends and competitor information for strategy formulation.

Developers use Web scraping Wikipedia for content creation, chatbots, and machine learning models.

Ethical and Efficient

Python enables compliance with Wikipedia’s scraping policies through APIs and structured extraction techniques. This ensures ethical data use while avoiding legal complications.

Scrape Wikipedia With Python to unlock insights, streamline operations, and power your projects with precise and reliable data. It’s a game- changer for organizations looking to maximize the potential of data.

Key Tools for Web Scraping Wikipedia Using Python

When you set out to Scrape Wikipedia With Python, having the right tools is crucial for efficient and effective data extraction. Below are some of the essential libraries and frameworks you can use:

1. BeautifulSoup

BeautifulSoup is one of the most popular Python libraries for web scraping Wikipedia. It allows you to parse HTML and XML documents, making navigating and searching the page structure easier. BeautifulSoup helps extract data from Wikipedia page tables, lists, and text content. It is known for its simplicity and flexibility in handling complex web structures.

2. Requests

The Requests library is used to send HTTP requests to Wikipedia and retrieve the HTML content of the page. It simplifies fetching data from a website and is essential for initiating the scraping process. With Requests, you can interact with Wikipedia’s servers and fetch the pages you want to scrape while seamlessly handling session management, authentication, and headers.

3. Pandas

Once the data is scraped, Pandas come in handy for organizing, cleaning, and analyzing the data. This library provides powerful data structures, like DataFrames, perfect for working with structured data from Wikipedia. Pandas can handle data transformation and cleaning tasks, making it an essential tool for post-scraping data processing.

4. Wikipedia API

Instead of scraping HTML pages, you can use the Wikipedia API to access structured data from Wikipedia directly. This API allows developers to request information in a structured format, such as JSON, making it faster and more efficient than parsing raw HTML content. The Wikipedia API is the recommended way to retrieve data from Wikipedia, ensuring compliance with the site's usage policies.

5. Selenium

When scraping pages with dynamic content, Selenium is the go-to tool. It automates web browsers, allowing you to interact with JavaScript-heavy websites. If Wikipedia pages load content dynamically, Selenium can simulate browsing actions like clicking and scrolling to extract the necessary data.

6. Scrapy

For larger, more complex scraping projects, Scrapy is a powerful and high-performance framework. It’s an open-source tool that enables scalable web scraping, allowing users to build spiders to crawl websites and gather data. Scrapy is ideal for advanced users building automated, large-scale scraping systems.

Utilizing these tools ensures that your Wikipedia Data Extraction is efficient, reliable, and scalable for any project.

Best Practices for Efficient Wikipedia Data Extraction

Regarding Wikipedia Data Extraction, adopting best practices ensures that your web scraping is efficient but also ethical and compliant with Wikipedia’s guidelines. Below are the key best practices for effective scraping:

1. Use the Wikipedia API

Rather than scraping HTML directly, it is best to leverage the Wikipedia API for structured data retrieval. The API allows you to request data in formats like JSON, making it faster and more reliable than parsing raw HTML. It also reduces the likelihood of errors and ensures you abide by Wikipedia's scraping guidelines. The API provides access to detailed articles, infoboxes, categories, and page revisions, making it the optimal way to extract Wikipedia data.

2. Respect Wikipedia’s Robots.txt

Always check Wikipedia's robots.txt file to understand its scraping policies. This file defines the rules for web crawlers, specifying which sections of the site are allowed to be crawled and scraped. Adhering to these rules helps prevent disruptions to Wikipedia’s infrastructure while ensuring your scraping activity remains compliant with its policies.

3. Optimize HTTP Requests

When scraping large volumes of data, optimizing HTTP requests is crucial to avoid overloading Wikipedia’s servers. Implement rate limiting, ensuring your scraping activities are paced and don’t overwhelm the servers. You can introduce delays between requests or use exponential backoff to minimize the impact of scraping on Wikipedia’s resources.

4. Handle Edge Cases

Be prepared for pages with inconsistent formatting, missing data, or redirects. Wikipedia is a vast platform with a wide range of content, so not all pages will have the same structure. Implement error-handling mechanisms to manage missing data, broken links, or redirects. This will ensure your script doesn’t break when encountering such anomalies.

5. Parse Tables Effectively

Wikipedia is filled with well-structured tables that contain valuable data. Pandas is an excellent library for efficiently extracting and organizing tabular data. Using Pandas, you can easily convert the table data into DataFrames, clean it, and analyze it as required.

6. Focus on Ethical Scraping

Lastly, ethical scraping should always be a priority. Respect copyright laws, provide proper attribution for extracted data, and avoid scraping sensitive or proprietary information. Ensure that the data you collect is used responsibly, complies with Wikipedia’s licensing terms, and contributes to the greater community.

By following these best practices, you can ensure that your web scraping activities on Wikipedia using Python are both practical and ethical while maximizing the value of the extracted data.

Real-Life Use Cases for Web Scraping Wikipedia

1. Academic Research

Web scraping Wikipedia can be valuable for academic researchers, especially in linguistics, history, and social sciences. Researchers often need large datasets to analyze language patterns, historical events, or social dynamics. With its vast structured information repository, Wikipedia provides an excellent source for gathering diverse data points. For instance, linguists might scrape Wikipedia to study language usage across different cultures or periods, while historians might gather data on events, figures, or periods for historical analysis. By scraping specific articles or categories, researchers can quickly build extensive datasets that support their studies.

2. Business Intelligence

Wikipedia data extraction plays a crucial role in competitive analysis and market research for businesses. Companies often scrape Wikipedia to analyze competitors' profiles, industry trends, and company histories. This information helps businesses make informed strategic decisions. Organizations can track market dynamics and stay ahead of trends by extracting and analyzing data on companies' growth, mergers, key executives, or financial milestones. Wikipedia pages related to industry sectors or market reports can also provide real-time data to enhance business intelligence.

3. Machine Learning Projects

Wikipedia serves as a rich source of training data for machine learning projects. For natural language processing (NLP) models, scraping Wikipedia text enables the creation of large corpora to train models on tasks like sentiment analysis, language translation, or entity recognition. Wikipedia's diverse and well-structured content makes it ideal for building datasets for various NLP applications. For example, a machine learning model designed to detect language nuances could benefit significantly from scraping articles across different topics and languages.

4. Knowledge Graphs

Extract Wikipedia data to build knowledge graphs for AI applications. Knowledge graphs organize information in a structured way, where entities like people, places, events, and concepts are connected through relationships. Wikipedia's well-organized data and links between articles provide an excellent foundation for creating these graphs. Scraping Wikipedia helps populate these knowledge bases with data that can power recommendation systems, semantic search engines, or personalized AI assistants.

5. Content Creation

Content creators often use Wikipedia data collection to streamline their work. By scraping Wikipedia, content creators can quickly generate fact-checks, summaries, or references for their articles, blogs, and books. Wikipedia's structured data ensures the information is reliable and consistent, making it a go-to source for generating accurate and up-to-date content. Bloggers and journalists can use scraped data to support their writing, ensuring their content is well-researched and informative.

Through these use cases, it is clear that web scraping Wikipedia offers numerous possibilities across various industries, from academia to business intelligence to AI development.

Statistics for 2025: The Impact of Data Scraping

By 2025, the global web scraping market is anticipated to reach a staggering $10.7 billion, fueled by the increasing need for automated data collection tools across various industries. As businesses rely more on data to drive decisions, the demand for efficient and scalable scraping solutions continues to rise, making this a key growth sector in the tech world.

Wikipedia plays a significant role in this growth, as it receives over 18 billion page views per month, making it one of the richest sources of free, structured data on the web. With millions of articles spanning virtually every topic imaginable, Wikipedia is a goldmine for businesses and researchers looking to collect large amounts of information quickly and efficiently.

The impact of web scraping on business performance is substantial. Companies leveraging scraping tools for data-driven decision-making have reported profit increases of up to 30%. By automating the collection of crucial market intelligence—such as competitor pricing, product availability, or customer sentiment—businesses can make quicker, more informed decisions that lead to improved profitability and competitive advantage.

As the web scraping industry continues to evolve and expand, the volume of accessible data and the tools to harvest it will grow, further shaping how businesses and researchers operate in the future.

Case Study: Extracting Data for Market Analysis

Challenge

A leading media analytics firm faced a significant challenge in tracking public opinion and historical events for its trend analysis reports. They needed to gather structured data on various topics, including social issues, historical events, political figures, and market trends. The firm’s existing process of manually collecting data was time-consuming and resource-intensive, often taking weeks to gather and process relevant information. This delay affected their client’s ability to provide timely insights, ultimately hindering their market intelligence offerings.

Solution

The firm leveraged Python and the Wikipedia API for large-scale data extraction to overcome these challenges. Using Python’s powerful libraries, such as Requests and BeautifulSoup, combined with the Wikipedia API, the firm could automate the data extraction process and pull structured data from Wikipedia’s vast repository of articles. This allowed them to access relevant content from thousands of Wikipedia pages in a fraction of the time compared to traditional methods. The firm gathered data on historical events, public opinion trends, and key industry topics. They set up an automated system to scrape, clean, and organize the data into a structured format, which could then be used for in-depth analysis.

Outcome

The results were significant. The firm was able to build a dynamic database of market intelligence, providing clients with real-time insights. By automating the data collection process, they saved approximately 60% of the time it previously took to gather the same amount of data.

The firm was able to deliver trend analysis reports much faster, improving client satisfaction and strengthening its position as a leader in the media analytics industry. The successful implementation of this solution not only streamlined the firm’s data collection process but also enhanced its ability to make data-driven decisions and offer more actionable insights to its clients.

Challenges in Web Scraping Wikipedia

While web scraping Wikipedia offers great potential for data collection and analysis, several challenges need to be addressed to ensure an effective and compliant scraping process.

1. Dynamic Content

Wikipedia pages often contain dynamic content, such as tables, infoboxes, and images, which may not always be easily accessible through traditional scraping methods. In some cases, these elements are rendered dynamically by JavaScript or other scripting languages, making extracting the data in a structured format more difficult. To handle this, advanced parsing techniques or tools like Selenium may be required to interact with the page as it loads or to simulate user behavior. Additionally, API calls may be needed to retrieve structured data rather than scraping raw HTML, especially for complex elements such as tables.

2. Data Volume

Wikipedia is a vast repository with millions of articles and pages across various languages. Scraping large volumes of data from Wikipedia can quickly become overwhelming in terms of the data size and the complexity of processing it. Efficient data handling is essential to avoid performance bottlenecks. For example, optimizing scraping scripts to manage memory usage, store data efficiently, and perform incremental scraping can significantly improve the overall process. Additionally, large datasets may require robust storage solutions, such as databases or cloud storage, to organize and manage the extracted data.

3. Compliance

Wikipedia operates under strict ethical guidelines, and scraping must comply with these standards. This includes respecting robots.txt directives, which specify which pages or sections of the site are off-limits for scraping. Furthermore, adhering to Wikipedia’s licensing policies and giving proper attribution for the data extracted is vital to avoid copyright violations. Ensuring compliance with legal standards and maintaining ethical practices throughout the scraping process is crucial for long-term success and avoiding potential legal issues.

By understanding and addressing these challenges, businesses and researchers can scrape Wikipedia efficiently and responsibly, extracting valuable insights without compromising data quality or compliance.

Mobile App Scraping: An Extension of Data Collection

While web scraping services have long been famous for gathering data from websites, mobile app scraping is rapidly becoming an essential extension of modern data collection techniques. As mobile applications dominate the digital landscape, businesses realize the immense potential of extracting data directly from apps to enhance their competitive advantage and drive informed decision-making.

Unlike websites, mobile apps often feature data not publicly available on their corresponding websites, such as real-time inventory information, user reviews, personalized recommendations, and even app-specific pricing models. This unique data can give businesses a more granular view of their competitors and market trends, offering insights that are often harder to obtain through traditional scraping methods. For example, mobile apps for grocery delivery services, e-commerce platforms, and ride-sharing apps frequently have detailed information about pricing, promotions, and consumer behavior not displayed on their websites.

Mobile app scraping can also benefit industries that rely on real-time data. For instance, travel and tourism companies can scrape mobile apps for flight availability, hotel prices, and rental car data. Similarly, the e- commerce sector can extract product data from mobile shopping apps to keep track of stock levels, prices, and seasonal discounts.

However, scraping mobile apps presents unique challenges, such as dealing with app-specific APIs, handling dynamic content, and overcoming security measures like CAPTCHAs or rate limits. Despite these challenges, businesses that implement effective mobile app scraping strategies gain a competitive edge by accessing often overlooked or unavailable data through traditional web scraping.

By incorporating mobile app scraping into their data collection processes, businesses can unlock valuable insights, stay ahead of competitors, and ensure they have the most up-to-date information for market analysis and decision-making.

Conclusion

Web scraping is a powerful tool for businesses, and scraping Wikipedia with Python offers unparalleled opportunities to collect and analyze data efficiently. Whether you’re a researcher, business analyst, or developer, following the best practices outlined in this blog ensures successful data extraction while respecting Wikipedia’s guidelines.

Ready to streamline your data collection process? Partner with Web Data Crawler today for efficient, ethical, customizable solutions. From Web Scraping Services to APIs, we have the tools to meet your business needs. Explore our services and take your data strategy to the next level!

Originally Published At :

[jcmarchi]

Baidu restricts Google and Bing from scraping content for AI training

New Post has been published on https://thedigitalinsider.com/baidu-restricts-google-and-bing-from-scraping-content-for-ai-training/

Baidu restricts Google and Bing from scraping content for AI training

.pp-multiple-authors-boxes-wrapper display:none; img width:100%;

Chinese internet search provider Baidu has updated its Wikipedia-like Baike service to prevent Google and Microsoft Bing from scraping its content.

This change was observed in the latest update to the Baidu Baike robots.txt file, which denies access to Googlebot and Bingbot crawlers.

According to the Wayback Machine, the change took place on August 8. Previously, Google and Bing search engines were allowed to index Baidu Baike’s central repository, which includes almost 30 million entries, although some target subdomains on the website were restricted.

This action by Baidu comes amid increasing demand for large datasets used in training artificial intelligence models and applications. It follows similar moves by other companies to protect their online content. In July, Reddit blocked various search engines, except Google, from indexing its posts and discussions. Google, like Reddit, has a financial agreement with Reddit for data access to train its AI services.

According to sources, in the past year, Microsoft considered restricting access to internet-search data for rival search engine operators; this was most relevant for those who used the data for chatbots and generative AI services.

Meanwhile, the Chinese Wikipedia, with its 1.43 million entries, remains available to search engine crawlers. A survey conducted by the South China Morning Post found that entries from Baidu Baike still appear on both Bing and Google searches. Perhaps the search engines continue to use older cached content.

Such a move is emerging against the background where developers of generative AI around the world are increasingly working with content publishers in a bid to access the highest-quality content for their projects. For instance, relatively recently, OpenAI signed an agreement with Time magazine to access the entire archive, dating back to the very first day of the magazine’s publication over a century ago. A similar partnership was inked with the Financial Times in April.

Baidu’s decision to restrict access to its Baidu Baike content for major search engines highlights the growing importance of data in the AI era. As companies invest heavily in AI development, the value of large, curated datasets has significantly increased. This has led to a shift in how online platforms manage access to their content, with many choosing to limit or monetise access to their data.

As the AI industry continues to evolve, it’s likely that more companies will reassess their data-sharing policies, potentially leading to further changes in how information is indexed and accessed across the internet.

(Photo by Kelli McClintock)

See also: Google advances mobile AI in Pixel 9 smartphones

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

Tags: ai, content moderation, Google, microsoft, search engine

[ask-shamebats]

I understand your position on ai art, I have a different position just cuz after learning about ai in grad school (i took courses on computer vision, artificial intelligence, natural language processing, etc) i am against almost all forms of ai art and chat gpt because the way that the data was gathered is super unethical (in my opinion). I have other reasons why I'm against chat gpt and ai art, but that is the main reason. They scrape the internet and take all the images they can find, or just ask of the text. It's one thing when it's the Wikipedia corpus (a dataset widely used for research) and a completely different thing to me when they scrape ao3 and wattpad and ebooks and transcripts. Chat gpt is just plagiarism, but fancy. They are paid services that charge for their use of stolen data. Its a mind boggling amount of data needed to train ai. To create good results on that scale, you need more data than the widely available research data, you have to steal. And they just stole it from wherever. I am against ai art & chat gpt bc i don't think that those companies have a right to charge for their software when the software only works bc they stole so much data.

That's a valid stance to have, I don't really have a strong opinion on it. I get why ppl don't like the way the data was gathered but I also get that it would've been very difficult for researchers to get access to big enough volumes of data to train algorithms on in a way that would involve getting consent from everyone involved and also compensating them.

Like every technology, it has a lot of potential to be used unethically. Like cars. I fucking hate cars. I hate big oil. I hate the auto industry. I'd do anything to avoid ever owning a car and I'd prefer it if personal cars weren't a thing, they kill absurd amounts of ppl, make the world unsafe, kill the planet and are one of the main caused of child death. But I also realize that there's no going back. Cars are here & they're useful to a lot of ppl. Occasionally, I'll sit my ass down in one and even enjoy it. I won't pretend that I think they're good, but I also won't go around telling ppl they're horrible for owning or driving a car when they could be taking public transport.

It's way more useful to educate & push for a more ethics in an industry than just demonize it imo. Devs are at this point very aware of the ethical responsibility they have with AI & I have hope that it can go in a more positive direction if they're held accountable by the public & other ppl from the field.

[facebookfeedus]

Wikimedia will launch a paid service for big tech companies https://ift.tt/3tsK1N3

Photo by Altan Gocher / DeFodi Images via Getty Images

The Wikimedia Foundation is creating a new paid service for companies that draw on Wikipedia data. The foundation announced the news today via an article in Wired, and it’s planning to launch later in 2021. Wikimedia Enterprise, as it’s called, won’t change how current Wikipedia services work. Instead, it will offer new options for companies that use its content, a category including giants like Google and Facebook.

Wikimedia is still finalizing how Wikimedia Enterprise will operate. But broadly, it’s like a premium version of Wikipedia’s API — the tool that lets anybody scrape and re-host Wikipedia articles. Enterprise customers could get data delivered faster or formatted to meet their needs, for instance, or get new options for...

Continue reading…

[djnailsspasalonrayford]

7 skills for web developers

Web development is one of the fastest growing industries. In fact, it's predicted to grow 13% until 2026.

The figure also shows that there's (and will) be a lot of work to follow. But do you have the skills to stand out in the competition and get the job of your dreams?

There are many skills you will need to develop and create successful websites. Here are the 7 most important web development skills you need!

1.HTML / CSS As a web developer, you will need to understand the basics of coding and markup languages.

https://en.wikipedia.org/wiki/Web_developer

Of all markup languages, Hypertext Markup Language (HTML) is the standard.

The actual HTML forms every web page on the Internet as we know it. How a website functions depends on how the developer writes the HTML.

But for your site to actually render as a web page, you'll rely on CSS.

You cannot write HTML without CSS

Cascading Style Sheets (CSS) interpret documents written in markup language. They are a more stylized representation of the HTML language.

https://www.w3schools.com/html

CSS also describes how an HTML document will look visually like a web page. It sets the bricks for the font, color, and overall layout of the website.

Think of it this way: HTML builds the skeleton of a web page. CSS gives the website its style and look.

Most basic web development skills require mastery of HTML and CSS. Don't neglect their importance!

2.JavaScript Once you master HTML and CSS, you'll eventually want to learn JavaScript.

JavaScript is a higher level programming language. It makes the website more interactive and functional. Create a website for the future

The web development industry is taking off. Standards are growing higher and more rigid. And with that, there will be higher expectations for the websites you create and the customers you work for.

JavaScript will allow you to create a better experience for web users. With JavaScript, you can write special features directly to your web pages. These include (but are not limited to) a search bar, social media and video share buttons.

JavaScript complements HTML. Although HTML forms a basic web page, JavaScript gives it more life and functionality.

3.Photoshop As a web developer, you'll want to know your way around Photoshop. It will not only make your life easier, but also help you perform better and faster.

You'll have a lot of fun to edit, design and stylize your website with Photoshop. You can even design some banners and logos for clients throughout your career.

But your Photoshop skills will extend far beyond appearance.

Once you master Photoshop, you won't just learn how to translate and design code. You will also create multiple website mockup.

So in other words, you'll be mainly using Photoshop for website planning.

4.WordPress Nearly 75 million websites operate on WordPress alone. That's over 25% of the internet altogether.

WordPress is a free content management system. It's also great for both beginners and for established web developers too.

It is relatively easy to use because you can edit and modify web pages, add plugins, and run error testing. There is also a Yoast feature that will help you with SEO.

You will want to develop your website building skills using other platforms. But WordPress is not just a standard but also a linchpin in the web development world.

5. Analytical skills If your web developer skills are strong, you will create successful websites. But there is a marketing aspect for jobs that few people really understand.

Of course, the most successful websites are the most functional.

But consumer behavior is always changing. So your design, coding and development skills will always evolve to delight the ever-changing consumer.

Hence, web developers need a strong understanding of the consumer. Especially web consumers.

You will meet a wide range of audiences, niche markets and customers throughout your career. If you can understand your consumers as a whole, it will only help you create sales websites. Know your audience

There are several ways to understand web consumers. But the most concrete way to understand them is to hone their online behavior.

And that's where web analytics tools come in.

Fortunately, there are many tools available in the market to help you collect web stats. For example, there are Google Analytics, MOZ Keyword Explorer, and SEMRush.

With statistics on the web, you will better understand your specific target audience. Web statistics will tell you which keywords users search for and how long they stay on your site.

It's access to the mind and interests of your target audience. And with all this knowledge, you can create more engaging websites.

6.SEO Search engine optimization (SEO) is the driving force of modern marketing.

https://en.wikipedia.org/wiki/Search_engine_optimization

Nowadays, websites need SEO to attract traffic and secure leads. Most modern consumers find products and services through online searches. Sites that do not implement SEO will not render high enough on search engine results pages.

Page upload speed, domain name reliability and keyword content are just some of the SEO skills web developers can (and should) learn. Increase traffic to every website you create

Web developers can apply SEO to help their website rank better and attract more traffic.

find us: Search engine optimization, Web search engine, Nofollow, Web crawler, Google Search, Meta element, PageRank, Digital marketing, Web software, Digital media, Human–computer interaction, Online services, Information science, Computing, Technology, Information retrieval, Communication, Web crawlers, Advertising agencies, Web development, Information technology, E-commerce, Online databases, Web 2.0, Indexes, Aggregation websites, Search engine software, Cyberspace, Websites, Internet search, Web scraping, Internet, Internet search engines, Hypertext, World Wide Web, Spamdexing, Online advertising, Search engine marketing, Web services, Web technology, Marketing, Marketing software, Software, Search engine indexing, Business, Mass media, Human activities, Multimedia, Media manipulation, Cultural globalization, Digital technology, Search engine results page, Directories, Backlink, Reference works, Web applications, Pay-per-click, Bing (search engine), Electronic publishing, Centralized computing, Computer networks, Media technology, Information economy, Website, Computer archives, Google Penguin, Promotion and marketing communications, Service industries, Robots exclusion standard, Human communication, Information technology management, Advertising, Google Hummingbird, Web directory, Communication design, Data management, Googlebot, Spamming, Internet ages, Sitemaps, Intertextuality, Artificial intelligence, Index (publishing), Tag (metadata), Human–machine interaction, Web analytics, Wikipedia, Affiliate marketing, Site map, Canonical link element, Wikidata, History of Google, Web traffic, Public sphere, HTML element, Link farm, Metadata, Internet ethics, Computer data, Noindex, Chromium (web browser), Targeted advertising, Cloaking, Business economics, Local search engine optimisation, User interfaces, Written communication, Cybercrime, Lawsuit, Complaint, Web search query, Software development, Yahoo!, Web portals, Google, Digital marketing companies of the United States, Larry Page, Internet Protocol based network software, Library science, Information retrieval organizations, Alternate reality, Mobile search, Local search (Internet), Sergey Brin, Matt Cutts, Information management, Danny Sullivan (technologist), Software engineering, Databases, Alphabet Inc., Web content, Computer networking, Hypertext Transfer Protocol clients, Applications of cryptography, Wikimedia Commons, Telecommunications, Internet fraud, Social media marketing, Publishing, User agent, Domain name, Marketing strategy, Video search engine, Computer file, Design, Seznam.cz, Blog, Wikimedia Foundation, Network service, DMOZ, Digital display advertising, Social media, Mathematical optimization, Wikiversity, Internet technology companies of the United States, Google services, Grey hat, Google Panda, Keyword density, Ad blocking, Internet forum, Web developer, World Wide Web, Internet, Information technology management, Information economy, Digital media, Human–computer interaction, Information science, Digital technology, Information technology, Cyberspace, Technology, Web development, Software development, Hypertext, Software engineering, Software, Computing, Human activities, Product development, Computer programming, Technology development, Systems engineering, Computer engineering, Web application, Multimedia, Web software, Software project management, Programmer, Cultural globalization, Media technology, Computers, Electronic publishing, Web design, Intellectual works, User interfaces, Communication, JavaScript, Computer science, Information Age, Web technology, Mass media, Electronics industry, PHP, Websites, Web template system, Web 2.0, Information management, Wikipedia, Scripting language, Human communication, Server-side scripting, Application software, HTML, Active Server Pages, Adobe ColdFusion, Single-page application, Software architecture, Web server, Wikidata, React (web framework), JQuery, Internet ages, Ember.js, AngularJS, ColdFusion Markup Language, Computer data, System software, Web applications, Cascading Style Sheets, Intertextuality, Management, Centralized computing, Human–machine interaction, Wikimedia Commons, Front-end web development, Website, Business, Wikimedia Foundation, Design, Database, Online services, Java (programming language), Open-source movement, Web service, Python (programming language), Free content, Computer-related introductions, Server-side, Wikiversity, World Wide Web Consortium standards, Server (computing), Software framework, Front and back ends, Ruby (programming language), Information and communications technology, Artificial intelligence, Web standards, Software design, Free software, Perl, Computer networking, Systems science, Hypertext Transfer Protocol, Employment, IT infrastructure, World Wide Web Consortium, Knowledge representation, Systems architecture, Public sphere, Information retrieval, Computer-mediated communication, Distributed computing architecture, Wide area networks, PDF, Programming paradigms

[andreacaskey]

Data scraping tools for marketers who don’t know code

We have all been there before. You need the right data from a website for your next content marketing project. You have found your source websites, the data is just there waiting for you to grab it and then the challenge emerges. You have 500 pages and wonder how to extract all this data at once.

It doesn’t help if you have the data if you can’t grab it. Without proper data scraping software, you won’t get it.

If you are like me, you had to learn Python so Scrapy can get the job done for you. Alternatively, you have to learn XPath for Excel, which is also something that takes quite a bit of time.

And since time is our most precious commodity, there is software available that doesn’t require learning a line of code to complete this task.

I have tried the following software as they all provide a free account and quite a good number of features to get the job done for a small to medium data set.

Definition of data scraping

The definition of data scraping is:

“…a technique in which a computer program extracts data from human-readable output coming from another program.”

– Wikipedia

Essentially, you can crawl entire websites, extract pieces of information from several pages and download this information into a structured Excel file. This is what I have done recently to build a sharable piece of research.

Data scraping can be used in many projects, including the following:

Price-monitoring projects, where you want to keep track of price changes;

Lead generation, where you can download your leads information for sales analysis;

Influencers and bloggers outreach, when you need to get information about name, surname, email address, tel number usually from a directory of influencers;

Extracting data for your research on any topic and website, this is my most used need of data.

Parsehub

This is by far my favorite tool for crawling data on big publications and blogs. You can do very advanced data segmentation and crawling with Parsehub, to extract pieces of information for each page. With Parsehub, you can collect information about calendars, comments, infinite scrolling, infinite page numbers, drop downs, forms, javascript and text.

The main features are:

Great customer support

Fairly intuitive

Very fast (if you are not using proxies and VPN)

Easy to use interface

Octoparse

With the free Octoparse account, you can scrape up to 10,000 records. If you need more records and you are working on one data scraping project, Octoparse offers the project-based one-time fee for unlimited records. The other service that I really like about Octoparse is that they offer to scrape data for you. All you need to provide if the website and the data input you want to download, they do the rest.

The main features are:

Click to extract

Scrape behind a login and form

Scheduled extraction

Easy to use

Import.io

This tool is expensive for a single individual starting at $299/month but luckily, they offer a free account. The reason why it’s more costly is that you can do more than just organizing unstructured data. With Import.io you can also do these tasks:

Identify the URL where your data is located

Extract the hidden content

Prepare the data with 100+ spreadsheet-like formulas

Integrate to your business systems with their API

Visualize data with custom reports

As you can see, Import.io serves the entire project-cycle, from data collection to visualization.

Grepsr

What interests me about Grepsr is the opportunity to manage the data scraping projects with a project management tool available of users. This allows many applications for the scraping project since these projects usually are very complicated. With the messaging and tasks apps in Grepsr you can quickly grab all requirements, answer to tickets and speak directly to all stakeholders involved.

The other very useful feature is automation. Instead manually set up each scraping project, you can set it up once and set a rule to the software for scheduled scrapes.

All of these extra features also come at a higher price of $199/ month, which can be expensive for a single user. So Grepsr is more suitable for team and big data projects, rather than single individuals. The free version for small projects is an option in the Chrome app.

Conclusion

We use big data to make essential business decisions. Having a reliable partner that can automate tasks will save you time. Whether you are doing market research, monitoring price changes on Amazon and eBay (or even Google), grabbing information for your next blogger outreach project, data scraping software can help you. Just make sure you try and test each one of them before committing.

The post Data scraping tools for marketers who don’t know code appeared first on Search Engine Land.

Data scraping tools for marketers who don’t know code published first on https://likesandfollowersclub.weebly.com/

[lindarifenews]

Data scraping tools for marketers who don’t know code

We have all been there before. You need the right data from a website for your next content marketing project. You have found your source websites, the data is just there waiting for you to grab it and then the challenge emerges. You have 500 pages and wonder how to extract all this data at once.

It doesn’t help if you have the data if you can’t grab it. Without proper data scraping software, you won’t get it.

If you are like me, you had to learn Python so Scrapy can get the job done for you. Alternatively, you have to learn XPath for Excel, which is also something that takes quite a bit of time.

And since time is our most precious commodity, there is software available that doesn’t require learning a line of code to complete this task.

I have tried the following software as they all provide a free account and quite a good number of features to get the job done for a small to medium data set.

Definition of data scraping

The definition of data scraping is:

“…a technique in which a computer program extracts data from human-readable output coming from another program.”

– Wikipedia

Essentially, you can crawl entire websites, extract pieces of information from several pages and download this information into a structured Excel file. This is what I have done recently to build a sharable piece of research.

Data scraping can be used in many projects, including the following:

Price-monitoring projects, where you want to keep track of price changes;

Lead generation, where you can download your leads information for sales analysis;

Influencers and bloggers outreach, when you need to get information about name, surname, email address, tel number usually from a directory of influencers;

Extracting data for your research on any topic and website, this is my most used need of data.

Parsehub

This is by far my favorite tool for crawling data on big publications and blogs. You can do very advanced data segmentation and crawling with Parsehub, to extract pieces of information for each page. With Parsehub, you can collect information about calendars, comments, infinite scrolling, infinite page numbers, drop downs, forms, javascript and text.

The main features are:

Great customer support

Fairly intuitive

Very fast (if you are not using proxies and VPN)

Easy to use interface

Octoparse

With the free Octoparse account, you can scrape up to 10,000 records. If you need more records and you are working on one data scraping project, Octoparse offers the project-based one-time fee for unlimited records. The other service that I really like about Octoparse is that they offer to scrape data for you. All you need to provide if the website and the data input you want to download, they do the rest.

The main features are:

Click to extract

Scrape behind a login and form

Scheduled extraction

Easy to use

Import.io

This tool is expensive for a single individual starting at $299/month but luckily, they offer a free account. The reason why it’s more costly is that you can do more than just organizing unstructured data. With Import.io you can also do these tasks:

Identify the URL where your data is located

Extract the hidden content

Prepare the data with 100+ spreadsheet-like formulas

Integrate to your business systems with their API

Visualize data with custom reports

As you can see, Import.io serves the entire project-cycle, from data collection to visualization.

Grepsr

What interests me about Grepsr is the opportunity to manage the data scraping projects with a project management tool available of users. This allows many applications for the scraping project since these projects usually are very complicated. With the messaging and tasks apps in Grepsr you can quickly grab all requirements, answer to tickets and speak directly to all stakeholders involved.

The other very useful feature is automation. Instead manually set up each scraping project, you can set it up once and set a rule to the software for scheduled scrapes.

All of these extra features also come at a higher price of $199/ month, which can be expensive for a single user. So Grepsr is more suitable for team and big data projects, rather than single individuals. The free version for small projects is an option in the Chrome app.

Conclusion

We use big data to make essential business decisions. Having a reliable partner that can automate tasks will save you time. Whether you are doing market research, monitoring price changes on Amazon and eBay (or even Google), grabbing information for your next blogger outreach project, data scraping software can help you. Just make sure you try and test each one of them before committing.

The post Data scraping tools for marketers who don’t know code appeared first on Search Engine Land.

Data scraping tools for marketers who don’t know code published first on https://likesfollowersclub.tumblr.com/

[iwebscrapingblogs]

Which are the Top 10 Most Scraped Websites in 2023?

In the age of data-driven decision-making and automation, web scraping has become an essential tool for extracting valuable information from the internet. Whether it's for competitive analysis, market research, or staying up to date with the latest trends, web scraping has gained prominence across various industries. In 2023, several websites have found themselves at the center of this data extraction frenzy. In this article, we will explore the top 10 most scraped websites in 2023.

Amazon Amazon has long been a popular target for web scrapers. E-commerce businesses scrape Amazon to monitor product prices, gather customer reviews, and track market trends. The abundance of data on Amazon's platform makes it a goldmine for those seeking to gain a competitive edge.

Twitter Social media platforms are a treasure trove of real-time data. Twitter, in particular, is a hotbed for scraping due to its vast user base and the constant stream of tweets. Researchers, marketers, and journalists scrape Twitter to gather insights on trending topics and public sentiment.

LinkedIn LinkedIn is a valuable source of professional information. Job seekers and recruiters scrape LinkedIn to build databases of potential connections and candidates. Market researchers also use it to track industry trends and professional networking.

Instagram Instagram's visual nature makes it an attractive target for web scraping. Businesses and individuals scrape Instagram for influencer marketing, competitor analysis, and trend monitoring. The platform is a goldmine for image and video data.

Wikipedia Wikipedia is a comprehensive source of information on a wide range of topics. Researchers and data enthusiasts scrape Wikipedia to build datasets for natural language processing, academic research, and content creation. It's a knowledge hub that attracts scrapers.

Reddit As one of the largest online discussion platforms, Reddit is a prime target for web scrapers. Data scientists and marketers scrape Reddit to identify emerging trends, monitor user sentiment, and discover niche communities.

Google News Google News aggregates news articles from various sources. Media organizations and content curators scrape Google News to stay updated with the latest news developments. The platform is a vital source of news content.

Yelp Yelp is a popular source for restaurant and business reviews. Local businesses and marketers scrape Yelp to monitor customer feedback, gather business information, and track their online reputation.

IMDb The Internet Movie Database (IMDb) is a goldmine of information about movies, TV shows, and celebrities. Movie enthusiasts and entertainment industry professionals scrape IMDb for information on films, actors, and industry trends.

Etsy Etsy is a marketplace for handmade and vintage goods. E-commerce businesses and artists scrape Etsy to monitor product listings, pricing, and market trends. It's a valuable resource for those in the creative and e-commerce sectors.

Web scraping, when done ethically and in compliance with the terms of service of these websites, provides businesses and individuals with valuable data that can be used for a variety of purposes. However, it's essential to be aware of the legal and ethical considerations surrounding web scraping, as scraping without permission or in a disruptive manner can lead to legal consequences and damage a website's functionality.

Additionally, websites can implement measures to protect themselves from scraping, such as using CAPTCHAs, IP blocking, or anti-scraping technologies. It's essential for web scrapers to adapt to these challenges and use scraping tools responsibly.

In conclusion, web scraping plays a crucial role in the modern data landscape, and the top 10 most scraped websites in 2023 reflect the diverse range of data needs across industries. From e-commerce giants like Amazon to social media platforms like Twitter, these websites offer a wealth of information that is highly sought after. As web scraping continues to evolve, it's important for scrapers to operate within legal and ethical boundaries, respecting the rights and terms of service of the websites they scrape.

[ericfruits]

New models for new media

FOR months Twitter, the micro-blogging service, has received the kind of free attention of which most companies can only dream. Politicians, corporate bosses, activists and citizens turn to the platform to catch every tweet of America’s new president, who has become the service��s de facto spokesman. “The whole world is watching Twitter,” boasted Jack Dorsey (pictured), the company’s chief executive, as he presented its results on February 9th. He has little else to brag about.

But Donald Trump has not provided the kind of boost the struggling firm really needs. It reported slowing revenue growth and a loss of $167m. User growth has been sluggish, too: it added just 2m users in that period. Facebook added 72m. The day of the results, shares in Twitter dropped by 12%. Because news outlets around the world already report on Mr Trump’s most sensational tweets, many do not feel compelled to join the platform to discover them. Others are put off by mobs of trolls and reams of misinformation.

And not even Mr Trump could change the cold, hard truth about Twitter: that it can never be Facebook. True, it has become one of the most important services for public and political communication among its 319m monthly users. It played an important role in the Arab spring and movements such as Black Lives Matter. But the platform’s freewheeling nature makes it hard to spin gold from. In fact, really trying to do so—by packing Twitter feeds with advertising, say—would drive away users.

Business as unusual

Twitter’s latest results are likely to encourage those who think it should never have become a publicly listed company, and want it to consider alternate models of ownership, such as a co-operative. They view Twitter as a kind of public utility—a “people’s platform”—the management of which should concern public interests rather more than commercial ones. If the company were co-operatively owned by users, it would be released from short-term pressure to please its investors and meet earnings targets.

Though some co-ops have shown themselves resilient, they are generally thought to be less dynamic—a shortcoming of democratic governance. Yet Sasha Costanza-Chock, an activist who teaches at the Massachusetts Institute of Technology, believes that Twitter users could also come up with features that would rescue it from its most toxic elements, such as harassment and hate speech. Others envision a futuristic co-op—or, inevitably, “co-op 2.0”—in which responsibility is split between idealistic entrepreneurs, who control product innovation, and users, who have the say on such matters as data protection. Even if such models could be made to work, Twitter is unlikely to become a co-op soon: its market capitalisation still exceeds $12bn, an amount users can hardly dream of scraping together. Yet the debate about what to do with the service has stoked another, long-simmering discussion in the startup world: whether firms should always aim to go public. “We have become very myopic about what it means to be a corporation,” explains Albert Wenger, a partner at Union Square Ventures, a technology-investment firm. Armin Steuernagel, founder of Purpose Capital, a consultancy, says he sees more and more start-ups questioning whether they should opt for conventional ownership structures.

Options abound. Online, Etsy, Kickstarter and Wikipedia, among others, have pursued set-ups that allow them to keep their social benefit front-and-centre. But old media outlets can offer lessons too: many publications in Europe, including The Economist, have ownership structures that isolate them to some degree from commercial interests.

As for Twitter, it is likely to be snapped up once its value is low enough. Although the most likely buyer is another tech firm, surprises cannot be excluded. Users should start thinking like a traditional labour union, says Mr Wenger. If they stage a virtual walkout, they might have the bargaining power to change its governance structure. #Squadgoals.

This article appeared in the Business section of the print edition under the headline "#Twittertrouble"

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Frase Review [2020]: AI-Powered Content Marketing Tool

Frase.io Pros & Cons

Pros Cons ·      Saves you A LOT of time when researching and creating content briefs

·      Great SEO optimisation features that’s comparable to other tools in the market

·      The Frase AI chatbot provides a great way to engage and convert potential customers

·      New features are regularly added to the tool. Don’t be surprised that by the time you read this, there are more features added!

·      Fantastic customer service from the team and the founder himself

·      Easy to use with an excellent user interface and user experience

·      Lots of growth potential, especially as an on-page optimisation and keyword research tool

·      As Frase is pretty new, it can be pretty buggy at times

·      Some features are not as good and accurate as what’s available in the market

·      Basic plan can get expensive if you don’t need 30 documents a month

·      Lack of documentation

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  As a content writer, I sometimes feel that writing content can be such a drag. I have to research the topic, structure my content, and then start writing them.

Without something to solve this issue, I’d be taking a lot of time just to churn out a simple 1000-word article.

What makes it worse is that part of my SEO strategy revolves around content, and how can I get my keywords ranked if I don’t write more articles?

This is why I bought Frase, an AI-powered tool that cuts the time I need to write an article by 4 hours.

And in this post, I’ll be reviewing Frase.io.

    What is Frase.io?

Frase (or Frase.io) is a content marketing tool that generates content briefs for your chosen keywords.

Suppose you’re writing an article from scratch. Frase scrapes the top 20 websites in the Google search results and will automatically generate a content brief in 10 seconds with the best topics that you should talk about.

If you already have existing content, you can use Frase to optimise it as Frase will tell you the most important terms your competitors are using.

Either way, Frase will benchmark your article against the 20 websites ranking (or you can choose which ones to compare with) to identify topic gaps and missing terms.

All this is done with the power of artificial intelligence.

  Frase.io’s Key Features (And how you can use it)

Frase.io has quite a few key features to it despite being mainly a content marketing tool.

  Generate Topic Ideas

Using Frase, you can generate topic ideas on what to write about for your blog. There are a few ways to do this.

    Question Ideas

Firstly, under the “Question Ideas” tab, you can search for a broad keyword term, and Frase will show you a long list of commonly asked questions related to that chosen keyword.

This is useful as it helps you find topic ideas to write about that your customers are actually searching for. From this page, you can generate an article brief with just a click of a button!

    Frase Concept Map

Secondly, Frase has the ability to generate a concept map from a topic chosen. This concept map extracts topics from Wikipedia and connects related topics together, while also providing you a brief summary of the Wikipedia article.

From here, you can either read the full wiki or generate the article brief for the target keyword.

When I’m writing this, most of the questions come from Quora and Reddit, and only a handful of them are related. The concept map also requires a bit of exploring to do before you can find a topic to write on.

As Frase was created to reduce the time needed for you to write articles, it’s expected that these features are not the best.

However, it’s still an excellent feature to include, especially when running out of ideas/topics to write about. Therefore, it’s recommended that you use Frase alongside a keyword research tool.

    Content Research & Creation

As mentioned above, Frase helps by generating content briefs based on topics that are covered by your competitors. These are called “documents”.

In a document, there is a content editor where you can easily add, remove, or edit stuff.

Frase will also provide you with a brief competitor analysis regarding what your competition wrote about, topics covered, a summary of each topic, and common questions asked in their articles.

Frase will also provide you with statistics such as the average word count, average links, and average sections from these pages.

To the right of the content editor are tabs for you to browse statistics, news related to the keyword, and a tab for links, for external linking purposes.

If you’re outsourcing content, this functionality can be used to generate better content briefs for your writers. You no longer need to research and create a content brief from scratch ever again!

It is vital to take note that Frase.io DOES NOT create content for you. It merely uses AI to do research and identify content gaps.

However, the founders are planning to incorporate GPT-3 into Frase in the future, which means that it might actually be able to do this.

    Content Optimisation

What makes Frase attractive is that it not only saves you research time, but you can also optimise your existing content with it.

For your chosen keyword, Frase provides you with content optimisation analyses where it extracts the most used terms from your competitors’ articles and counts the number of times it’s being used.

These keyword suggestions are powerful because Google has already made up their mind on what they want to see on the first page. Replicating and improving the results you see is a strong way to beat your competition.

This method is called TF*IDF.

Frase has a proprietary scoring system (0-100%) called “Topic Score” that scores your content against those in search engines. There is also an average competitor topic score for you to refer to.

You should look into getting a content score higher than it by writing better content. With your optimised content, you are likely to see positive ranking changes.

    Frase Answer Engine

The Frase Answer Engine is an AI-powered chatbot that uses your website’s content to answer your visitors’ common questions. Frase will crawl your website and break it down into different sections, using it as information to answer queries.

The Answer Engine is a dedicated chatbot software that is super easy to install. All you have to do is to insert a script into your website’s header. If you know how to install Google Analytics, you’ll definitely know how to install this.

I love this chat option because overall, it has a user-friendly interface where you can customise its look to how you want it. There is also a test sandbox where you can train your chatbot to answer your visitors’ questions.

    In the analytics section, you can see the questions asked by your website visitors, and the score given for the usefulness of the AI chatbot’s answers provided. If the answers are wrong, or you don’t like how it is being answered, you can train the Answer Engine accordingly.

The Answer Engine also allows you to capture emails, useful for you to follow up with a potential sale!

Oh, not forgetting that the Answer Engine also has a live chat option where potential customers can contact you promptly.

    Frase Integrations

According to their website, Frase provides integrations with WordPress, HubSpot COS, MailChimp, Google Drive, Google Search Console, and HubSpot HRM.

However, as of writing this, the only integration options I have are with HubSpot’s CRM and Google Search Console. According to their Facebook group, they are currently fixing their other integrations due to bug-related issues.

  Hubspot Integration

The HubSpot CRM integration allows you to send lead information obtained from the Answer Engine automatically to HubSpot.

  Google Search Console Integration

The Google Search Console integration is rather interesting. Frase firstly extracts the keywords you’re ranking for and clusters them by topics. These are then ranked according to its impressions, position, clicks, and CTR.

Based on this data, Frase recommends an action that you should take to improve your search traffic. Either you can create a new document, optimise a current one, or track it.

Unfortunately, I’m not able to find any substantial documentation on how to best use this information.

I understand why I’m being recommended to optimise or track each cluster’s performance, but why would I need to create a new article if I’m already ranking for the keywords? Hmm…

    Frase’s Support

Although not technically a feature of Frase, I do have to say that their support deserves a shoutout in this review. Inside Frase’s private Facebook group, many users raise issues, bugs, and recommendations to improve the tool.

Tommy Rgo, the founder of Frase, is pretty darn active in responding to all of these. He raises bugs and issues quickly to his team. Depending on the severity, they can have a solution for you within 24 hours.

Sometimes, the same question gets asked frequently in the group, and Tommy never fails to reply patiently and politely.

Recommendations are also taken seriously. Many of them have already been added as additional features or are currently in the pipeline.

However, what could use improvement is their knowledge base on how to use the tool. Many users, including myself, find that it’s pretty shallow and have to resort to their Facebook group to get help from other users.

  Pricing

I got Frase through an AppSumo lifetime deal for only US$69, although I’m not too sure if this deal will come back anytime soon.

However, Frase provides a free trial where you can do unlimited question research, create 5 documents, 1 crawl through Google Search Console, and a 30-day trial of their Answer Engine.

Otherwise, below are their monthly and annual pricing for various plans.

Plan Type

Basic Plan Growth Plan Answer Engine Pricing US$39.99/month billed annually (12% savings)

or

US$44.99/month

US$99.99/month billed annually (13% savings)

or

US$114.99/month

US$199.99/month billed annually Number of Users 1 user 3 users ($15 per extra) 3 users ($15 per extra) Number of documents 30 documents/month Unlimited documents Unlimited documents Answers – – 500 answers/month per Answer Engine Additional Answers – –

$50 per extra 100 answers

  *EDIT Frase.io just launched a LIFETIME 50% off for the next 500 new customers. This deal is expected to be fully redeemed within the next 5 days! Click on the button below and use “forever50” as a coupon during your checkout now!

Get Frase.io Now!

  What others think about Frase.io

I’m not the only one who loves how Frase has saved my time and money when creating content. Many of their users are also great fans and have left reviews on various platforms.

Rated 4.7/5.0 from 22 reviews on G2 Crowd Review

    Rated 4.7/5.0 from 108 reviews on Appsumo.com

    Frase.io is also used by companies like Neil Patel, Drift, eToro, and Microsoft.

Here are also a few of the many comments left inside their Facebook group.

    Frase Alternatives

There are only a few other tools that are true Frase alternatives. Such tools are MarketMuse and ClearScope.

Through some research, MarketMuse and ClearScope seem to have a far superior keyword research feature built-in. Frase’s “Question Ideas” and “Concept Map” could use some improvements.

However, the main features that Frase offers (content research and optimisation) look to be on par with these alternatives.

What sets the difference between Frase, MarketMuse, and ClearScope is definitely the pricing. MarketMuse starts at US$500/month, while ClearScope starts at US$350/month.

This is wayyyy more expensive than Frase’s basic plan of US$44.99/month, making Frase the better option if you’re looking for value.

  Conclusion

Despite the cons that Frase has, I believe that it is definitely one of the best purchases I made as a content writer.

As a new tool in the market, Frase has proved to be an invaluable tool that removes the hassle in content writing. Frase is also continually improving and developing itself, making it almost irresistible to not make a purchase.

With the development of GPT-3, you just might be able to expect Frase to start offering AI-generated content.

However, if you currently face the below problems:

You find that writing articles take up too much of your time

You lose motivation easily when writing content

You need to save time in doing research

You want to improve your on-page SEO

You have a lot of writers and need to generate content briefs FAST

You’re looking to maximise your budget

  Frase.io is definitely a good fit for you.

*EDIT Frase.io just launched a LIFETIME 50% off for the next 500 new customers. This deal is expected to be fully redeemed within the next 5 days! Click on the button below and use “forever50” as a coupon during your checkout now!

Get Frase.io Now!

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