#twitterdata

[trackmyhashtagsblog]

Do you want to see anyone's past tweets, so you have a one-stop solution here the best tool - TrackMyHashtag. It is an AI-based analytical tool that provides hashtag analytics of historical Twitter datasets.

[amaralaurent]

Check anyone's Twitter following list in depth!📊

https://www.followersanalysis.com/blog/how-to-export-twitter-following-list-to-csv-excel/

[twexlist-blog]

Export your twitter data to maintain the account.

[crypscrow]

#dailycryptoupdate #cryptonews #crypscrow #cryptoanalysis #ripple #bullish #coinmetrics #twitterdata #whitepaper #libra #facebook #nourielroubini #globalcoin #stonewalled #finra #limbo #madcrypto #securities https://www.instagram.com/p/By1pDbngWb0/?igshid=565dl0vpkjps

[divierteteetb]

¿Quieres una copia de tus contenidos de Twitter?

La opción llega con una actualización de la app en la que ahora podrás tener una copia de seguridad de todo lo que tengas almacenado allí.

Aquí te enseñamos cómo hacerlo.

Twitter se ha actualizado y ahora nos permite tener acceso a todos nuestros contenidos, desde nuestros tweets, pasando por fotos y videos hasta la lista de nuestros seguidores. 

Esta es la mayor copia de seguridad que puedes obtener de tu cuenta en Twitter, una opción que evita que tu información sea víctima de la volatilidad de la Internet y se pierda en la gran nube de datos.

Te enseñamos cómo hacerlo:

-Abre tu cuenta de Twitter desde la versión web en tu computador. -Ve al menú y a la opción “Configuración y Privacidad”. -Desplázate a la parte inferior de la pantalla hasta la opción “Tu archivo de Tweets”. -Haz clic en “Solicita tu archivo”

Allí veras un mensaje en el cual te informan que se enviará un link de descarga al correo que tienes registrado.

¡Y listo!

Recuerda que para descargar tus datos necesitas la mejor conexión a Internet por eso tu mejor opción es conectarte con la Fibra Óptica de ETB. Conéctate con todo lo que más te gusta con nuestra red Móvil 4G ETB y su red de Banda Ancha ETB.

Si aún no cuentas con nuestros servicios, escríbenos través de nuestras redes sociales para adquirirlos y entérate  de todas las ofertas y planes que tenemos para ti, encuéntranos en Facebook como: ETB.Colombia, en Twitter: @ETBsoluciones y en YouTube: ETBCorporativo

[actosoluions]

How to Scrape Tweets Data by Location Using Python and snscrape?

In this blog, we will take a comprehensive look into scraping Python wrapper and its functionality and specifically focus on using it to search for tweets based on location. We will also delve into why the wrapper may not always perform as expected. Let's dive in

snscrape is a remarkable Python library that enables users to scrape tweets from Twitter without the need for personal API keys. With its lightning-fast performance, it can retrieve thousands of tweets within seconds. Moreover, snscrape offers powerful search capabilities, allowing for highly customizable queries. While the documentation for scraping tweets by location is currently limited, this blog aims to comprehensively introduce this topic. Let's delve into the details:

Introduction to Snscrape: Snscrape is a feature-rich Python library that simplifies scraping tweets from Twitter. Unlike traditional methods that require API keys, snscrape bypasses this requirement, making it accessible to users without prior authorization. Its speed and efficiency make it an ideal choice for various applications, from research and analysis to data collection.

The Power of Location-Based Tweet Scraping: Location-based tweet scraping allows users to filter tweets based on geographical coordinates or place names. This functionality is handy for conducting location-specific analyses, monitoring regional trends, or extracting data relevant to specific areas. By leveraging Snscrape's capabilities, users can gain valuable insights from tweets originating in their desired locations.

Exploring Snscrape's Location-Based Search Tools: Snscrape provides several powerful tools for conducting location-based tweet searches. Users can effectively narrow their search results to tweets from a particular location by utilizing specific parameters and syntax. This includes defining the search query, specifying the geographical coordinates or place names, setting search limits, and configuring the desired output format. Understanding and correctly using these tools is crucial for successful location-based tweet scraping.

Overcoming Documentation Gaps: While snscrape is a powerful library, its documentation on scraping tweets by location is currently limited. This article will provide a comprehensive introduction to the topic to bridge this gap, covering the necessary syntax, parameters, and strategies for effective location-based searches. Following the step-by-step guidelines, users can overcome the lack of documentation and successfully utilize snscrape for their location-specific scraping needs.

Best Practices and Tips: Alongside exploring Snscrape's location-based scraping capabilities, this article will also offer best practices and tips for maximizing the efficiency and reliability of your scraping tasks. This includes handling rate limits, implementing error-handling mechanisms, ensuring data consistency, and staying updated with any changes or updates in Snscrape's functionality.

Introduction of snscrape Using Python

In this blog, we’ll use tahe development version of snscrape that can be installed withpip install git+https://github.com/JustAnotherArchivist/snscrape.git

Note: this needs Python 3.8 or latest

Some familiarity of the Pandas module is needed.

We encourage you to explore and experiment with the various features of snscrape to better understand its capabilities. Additionally, you can refer to the mentioned article for more in-depth information on the subject. Later in this blog, we will delve deeper into the user field and its significance in tweet scraping. By gaining a deeper understanding of these concepts, you can harness the full potential of snscrape for your scraping tasks.

Advanced Search Features

In this code snippet, we define the search query as "pizza near:Los Angeles within:10km", which specifies that we want to search for tweets containing the word "pizza" near Los Angeles within a radius of 10 km. The TwitterSearchScraper object is created with the search query, and then we iterate over the retrieved tweets and print their content.

Feel free to adjust the search query and radius per your specific requirements.

For comparing results, we can utilize an inner merging on two DataFrames:common_rows = df_coord.merge(df_city, how='inner')

That returns 50 , for example, they both have the same rows.

What precisely is this place or location?

When determining the location of tweets on Twitter, there are two primary sources: the geo-tag associated with a specific tweet and the user's location mentioned in their profile. However, it's important to note that only a small percentage of tweets (approximately 1-2%) are geo-tagged, making it an unreliable metric for location-based searches. On the other hand, many users include a location in their profile, but it's worth noting that these locations can be arbitrary and inaccurate. Some users provide helpful information like "London, England," while others might use humorous or irrelevant descriptions like "My Parents' Basement."

Despite the limited availability and potential inaccuracies of geo-tagged tweets and user profile locations, Twitter employs algorithms as part of its advanced search functionality to interpret a user's location based on their profile. This means that when you look for tweets through coordinates or city names, the search results will include tweets geotagged from the location and tweets posted by users who have that location (or a location nearby) mentioned in their profile.

To illustrate the usage of location-based searching on Twitter, let's consider an example. Suppose we perform a search for tweets near "London." Here are two examples of tweets that were found using different methods:

The first tweet is geo-tagged, which means it contains specific geographic coordinates indicating its location. In this case, the tweet was found because of its geo-tag, regardless of whether the user has a location mentioned in their profile or not.

The following tweet isn’t geo-tagged, which means that it doesn't have explicit geographic coordinates associated with it. However, it was still included in the search results because a user has given a location in the profile that matches or is closely associated with London.

When performing a location-based search on Twitter, you can come across tweets that are either geo-tagged or have users with matching or relevant locations mentioned in their profiles. This allows for a more comprehensive search, capturing tweets from specific geographic locations and users who have declared their association with those locations.

Get Location From Scraped Tweets

If you're using snscrape to scrape tweets and want to extract the user's location from the scraped data, you can do so by following these steps. In the example below, we scrape 50 tweets within a 10km radius of Los Angeles, store the data in a DataFrame, and then create a new column to capture the user's location.

If It Doesn’t Work According to Your Expectations

The use of the near: and geocode: tags in Twitter's advanced search can sometimes yield inconsistent results, especially when searching for specific towns, villages, or countries. For instance, while searching for tweets nearby Lewisham, the results may show tweets from a completely different location, such as Hobart, Australia, which is over 17,000 km away.

To ensure more accurate results when scraping tweets by locations using snscrape, it is recommended to use the geocode tag having longitude & latitude coordinates, along with a specified radius, to narrow down the search area. This approach will provide more reliable and precise results based on the available data and features.

Conclusion

In conclusion, the snscrape Python module is a valuable tool for conducting specific and powerful searches on Twitter. Twitter has made significant efforts to convert user input locations into real places, enabling easy searching by name or coordinates. By leveraging its capabilities, users can extract relevant information from tweets based on various criteria.

For research, analysis, or other purposes, snscrape empowers users to extract valuable insights from Twitter data. Tweets serve as a valuable source of information. When combined with the capabilities of snscrape, even individuals with limited experience in Data Science or subject knowledge can undertake exciting projects.

Happy scrapping!

For more details, you can contact Actowiz Solutions anytime! Call us for all your mobile app scraping and web scraping services requirements.

sources :https://www.actowizsolutions.com/how-to-scrape-tweets-data-by-location-using-python-and-snscrape.php

[dibya963-blog]

Get the complete list of Twitter Followers & Following Data as well as 3200 tweets of any Twitter account in CSV/Excel along with a comprehensive analytics PDF. Twitter follower analysis includes Most active and popular followers, Followers' last tweet date, Followers' account age, etc.

[recentanimenews]

JUJUTSU KAISEN, Demon Slayer, in Top 10 Tweeted Shows of 2021 Worldwide

  2021 has been a great year for television, whether you're looking at shows that come from the United States, South Korea, Japan, and all over the rest of the world. Twitter revealed today what television shows were the most tweeted around the world throughout the year, with anime making up nearly half of the entire list! 

  While it's not first, JUJUTSU KAISEN came in at 2nd place (above Netflix's Squid Game!), with the ever-popular Demon Slayer: Kimetsu no Yaiba at fourth place, Tokyo Revengers at 7th place, and then the sleeper hit Pui Pui Molcar rounding off the anime representation at 10th place. 

    A little competition never hurt -- at least when it comes to getting people to Tweet about TV. The most Tweeted shows of 2021 include reality TV competition favorites Big Brother Brasil and A Fazenda 13, in addition to K-drama Squid Game. pic.twitter.com/Wta20VoFHx

— Twitter Data (@TwitterData) December 9, 2021

    Here's the full ranking:

  10. Pui Pui Molcar

9. Sen Cal Kapimi

8. In the Soop

7. Tokyo Revengers

6. A Fazenda 13

5. Wandavision

4. Demon Slayer: Kimetsu no Yaiba

3. Squid Game

2. JUJUTSU KAISEN

1. Big Brother Brasil

  What was the show you most spoke about over 2021? Twitter or otherwise? Let us know down in the comments!

  Source: Twitter

  ---- 

Daryl Harding is a Japan Correspondent for Crunchyroll News. He also runs a YouTube channel about Japan stuff called TheDoctorDazza, tweets at @DoctorDazza, and posts photos of his travels on Instagram.

By: Daryl Harding

[gobindkhalsa]

With Australia's marriage equality postal vote results coming out tomorrow, and i figured out how to use twitter's APIs, I took a sample of 2500 popular & recent tweets and created a word collage in RStudio! But i haven't figured out how to adjust the colours to produce a less jarring image. Then i figured i might as well do some sentiment analysis to see how a computer algorithm thinks people on twitter feel about the vote based on their choice of words. Most who tweeted in the sample were had positive sentiments! #marriageEquality #hopeItsYES #dataScience #data #twitterData #graphs #wordCollage (at Melbourne, Victoria, Australia)

[bobsinm]

Animation - 3 Days of Geotagged Tweets

[trackmyhashtagsblog]

Know More about the Twitter Competitor Analysis,  Twitter’s competitor analysis is studying and decoding the strategies used by your competitors to get high Twitter engagements. Read the blog to know more: https://cutt.ly/swM9K9AB

[amaralaurent]

Export Twitter Followers and Following List to an Excel/CSV File #downloadfollowers #exporttwitterfollowers #twitterdata

https://www.followersanalysis.com/

[trackmyhash]

Twitter Dataset

[twexlist-blog]

Twitter offers a listing service. With this list, we are able to manage Twitter groups in order to better filter and prioritize the information.

[cwdigimark]

ABOUT YOUR ACTIVITY DASHBOARD

ABOUT YOUR ACTIVITY DASHBOARD - an article I am reading while studying Social Media Marketing!

Read more here: https://lnkd.in/eFEWfCQn

#b#hashtagcampaign#twitteranalytics#digitalmarketers#digitalmarketing#sentimentanalysis#socialmediaanalyzer#socialmedia#influencermarketing#twitter#twitteranalyzer#twitterengagement#twitterdashboard#twitterdata#botlikeliness#twitterimpressions#contentstrategy#hashtags#marketing#marketingadvice#twitterinfluencers#marketingagency#detectbots#socialmediadata#socialmediadashboards#twitterbots#osint#osinttools#marketingblog#bmarketing