In 2022, the total number of verified Twitter profiles in Spain was *****, an increase of over *** accounts compared to the previous year. There were a total of **** million Twitter users in Spain in 2022.

As of December 2022, X/Twitter's audience accounted for over *** million monthly active users worldwide. This figure was projected to ******** to approximately *** million by 2024, a ******* of around **** percent compared to 2022.

Social network X/Twitter is particularly popular in the United States, and as of February 2025, the microblogging service had an audience reach of 103.9 million users in the country. Japan and the India were ranked second and third with more than 70 million and 25 million users respectively. Global Twitter usage As of the second quarter of 2021, X/Twitter had 206 million monetizable daily active users worldwide. The most-followed Twitter accounts include figures such as Elon Musk, Justin Bieber and former U.S. president Barack Obama. X/Twitter and politics X/Twitter has become an increasingly relevant tool in domestic and international politics. The platform has become a way to promote policies and interact with citizens and other officials, and most world leaders and foreign ministries have an official Twitter account. Former U.S. president Donald Trump used to be a prolific Twitter user before the platform permanently suspended his account in January 2021. During an August 2018 survey, 61 percent of respondents stated that Trump's use of Twitter as President of the United States was inappropriate.

As of August 2023, X (formerly Twitter) CEO Elon Musk was the most followed person on the platform, with little over 140 million followers. Additionally, former U.S. President Barack Obama amassed 132 million followers on the micro-blogging service. In April 2023, Musk changed Twitter's legal name to X Corp.

Celebrities and Twitter

Founded in 2006 as Twitter, X is an online social networking and microblogging service that allows users to post text-based status updates and messages of up to 280 characters in length. As of the fourth quarter of 2020, X had 192 million monetizable daily active users (mDAU) worldwide.

X provides a near-instant access channel to celebrities. The majority of the top ten most-followed X accounts are entertainers who use the medium to communicate with fans, spread relevant news regarding their work or work on their public image. The near-instant gratification through a stream of direct updates from celebrities or personalities as well as the feeling of belonging to a particular group of fans is a popular reason for social media users to use X. In order to establish authenticity of identity on X, accounts of people from high-interest areas such as music, fashion, entertainment, politics, media, business or other areas as well as individuals at high risk of impersonation are verified by X. The verification badge symbolizes that the account is maintained by a legitimate source.

Major sporting events and industry award shows such as the Super Bowl, the Grammy Awards or Academy Awards generate lots of online buzz on X. The online discussion allows users to participate in the success of celebrities who often post behind-the-scenes photo tweets or commentaries. On-set or in-concert tweets are further methods of celebrities enhancing their appeal, and level of fan interaction.

General Description

This dataset comprises 4,038 tweets in Spanish, related to discussions about artificial intelligence (AI), and was created and utilized in the publication "Enhancing Sentiment Analysis on Social Media: Integrating Text and Metadata for Refined Insights," (10.1109/IE61493.2024.10599899) presented at the 20th International Conference on Intelligent Environments. It is designed to support research on public perception, sentiment, and engagement with AI topics on social media from a Spanish-speaking perspective. Each entry includes detailed annotations covering sentiment analysis, user engagement metrics, and user profile characteristics, among others.

Data Collection Method

Tweets were gathered through the Twitter API v1.1 by targeting keywords and hashtags associated with artificial intelligence, focusing specifically on content in Spanish. The dataset captures a wide array of discussions, offering a holistic view of the Spanish-speaking public's sentiment towards AI.

Dataset Content

ID: A unique identifier for each tweet.

text: The textual content of the tweet. It is a string with a maximum allowed length of 280 characters.

polarity: The tweet's sentiment polarity (e.g., Positive, Negative, Neutral).

favorite_count: Indicates how many times the tweet has been liked by Twitter users. It is a non-negative integer.

retweet_count: The number of times this tweet has been retweeted. It is a non-negative integer.

user_verified: When true, indicates that the user has a verified account, which helps the public recognize the authenticity of accounts of public interest. It is a boolean data type with two allowed values: True or False.

user_default_profile: When true, indicates that the user has not altered the theme or background of their user profile. It is a boolean data type with two allowed values: True or False.

user_has_extended_profile: When true, indicates that the user has an extended profile. An extended profile on Twitter allows users to provide more detailed information about themselves, such as an extended biography, a header image, details about their location, website, and other additional data. It is a boolean data type with two allowed values: True or False.

user_followers_count: The current number of followers the account has. It is a non-negative integer.

user_friends_count: The number of users that the account is following. It is a non-negative integer.

user_favourites_count: The number of tweets this user has liked since the account was created. It is a non-negative integer.

user_statuses_count: The number of tweets (including retweets) posted by the user. It is a non-negative integer.

user_protected: When true, indicates that this user has chosen to protect their tweets, meaning their tweets are not publicly visible without their permission. It is a boolean data type with two allowed values: True or False.

user_is_translator: When true, indicates that the user posting the tweet is a verified translator on Twitter. This means they have been recognized and validated by the platform as translators of content in different languages. It is a boolean data type with two allowed values: True or False.

Cite as

Guerrero-Contreras, G., Balderas-Díaz, S., Serrano-Fernández, A., & Muñoz, A. (2024, June). Enhancing Sentiment Analysis on Social Media: Integrating Text and Metadata for Refined Insights. In 2024 International Conference on Intelligent Environments (IE) (pp. 62-69). IEEE.

Potential Use Cases

This dataset is aimed at academic researchers and practitioners with interests in:

Sentiment analysis and natural language processing (NLP) with a focus on AI discussions in the Spanish language.

Social media analysis on public engagement and perception of artificial intelligence among Spanish speakers.

Exploring correlations between user engagement metrics and sentiment in discussions about AI.

Data Format and File Type

The dataset is provided in CSV format, ensuring compatibility with a wide range of data analysis tools and programming environments.

License

The dataset is available under the Creative Commons Attribution 4.0 International (CC BY 4.0) license, permitting sharing, copying, distribution, transmission, and adaptation of the work for any purpose, including commercial, provided proper attribution is given.

Cite as

Guerrero-Contreras, G., Balderas-Díaz, S., Serrano-Fernández, A., & Muñoz, A. (2024, June). Enhancing Sentiment Analysis on Social Media: Integrating Text and Metadata for Refined Insights. In 2024 International Conference on Intelligent Environments (IE) (pp. 62-69). IEEE.

General Description

This dataset comprises 4,038 tweets in Spanish, related to discussions about artificial intelligence (AI), and was created and utilized in the publication "Enhancing Sentiment Analysis on Social Media: Integrating Text and Metadata for Refined Insights," (10.1109/IE61493.2024.10599899) presented at the 20th International Conference on Intelligent Environments. It is designed to support research on public perception, sentiment, and engagement with AI topics on social media from a Spanish-speaking perspective. Each entry includes detailed annotations covering sentiment analysis, user engagement metrics, and user profile characteristics, among others.

Data Collection Method

Tweets were gathered through the Twitter API v1.1 by targeting keywords and hashtags associated with artificial intelligence, focusing specifically on content in Spanish. The dataset captures a wide array of discussions, offering a holistic view of the Spanish-speaking public's sentiment towards AI.

Dataset Content

ID: A unique identifier for each tweet.

text: The textual content of the tweet. It is a string with a maximum allowed length of 280 characters.

polarity: The tweet's sentiment polarity (e.g., Positive, Negative, Neutral).

favorite_count: Indicates how many times the tweet has been liked by Twitter users. It is a non-negative integer.

retweet_count: The number of times this tweet has been retweeted. It is a non-negative integer.

user_verified: When true, indicates that the user has a verified account, which helps the public recognize the authenticity of accounts of public interest. It is a boolean data type with two allowed values: True or False.

user_default_profile: When true, indicates that the user has not altered the theme or background of their user profile. It is a boolean data type with two allowed values: True or False.

user_has_extended_profile: When true, indicates that the user has an extended profile. An extended profile on Twitter allows users to provide more detailed information about themselves, such as an extended biography, a header image, details about their location, website, and other additional data. It is a boolean data type with two allowed values: True or False.

user_followers_count: The current number of followers the account has. It is a non-negative integer.

user_friends_count: The number of users that the account is following. It is a non-negative integer.

user_favourites_count: The number of tweets this user has liked since the account was created. It is a non-negative integer.

user_statuses_count: The number of tweets (including retweets) posted by the user. It is a non-negative integer.

user_protected: When true, indicates that this user has chosen to protect their tweets, meaning their tweets are not publicly visible without their permission. It is a boolean data type with two allowed values: True or False.

user_is_translator: When true, indicates that the user posting the tweet is a verified translator on Twitter. This means they have been recognized and validated by the platform as translators of content in different languages. It is a boolean data type with two allowed values: True or False.

Potential Use Cases

This dataset is aimed at academic researchers and practitioners with interests in:

Sentiment analysis and natural language processing (NLP) with a focus on AI discussions in the Spanish language.

Social media analysis on public engagement and perception of artificial intelligence among Spanish speakers.

Exploring correlations between user engagement metrics and sentiment in discussions about AI.

Data Format and File Type

The dataset is provided in CSV format, ensuring compatibility with a wide range of data analysis tools and programming environments.

License

The dataset is available under the Creative Commons Attribution 4.0 International (CC BY 4.0) license, permitting sharing, copying, distribution, transmission, and adaptation of the work for any purpose, including commercial, provided proper attribution is given.

UPDATE: Due to new Twitter API conditions changed by Elon Musk, now it's no longer free to use the Twitter (X) API and the pricing is 100 $/month in the hobby plan. So my automated ETL notebook stopped from updating new tweets to this dataset on May 13th 2023.

This dataset is was updated everyday with the addition of 1000 tweets/day containing any of the words "ChatGPT", "GPT3", or "GPT4", starting from the 3rd of April 2023. Everyday's tweets are uploaded 24-72h later, so the counter on tweets' likes, retweets, messages and impressions gets enough time to be relevant. Tweets are from any language selected randomly from all hours of the day. There are some basic filters applied trying to discard sensitive tweets and spam.

This dataset can be used for many different applications regarding to Data Analysis and Visualization but also NLP Sentiment Analysis techniques and more.

Consider upvoting this Dataset and the ETL scheduled Notebook providing new data everyday into it if you found them interesting, thanks! 🤗

Columns Description:

• tweet_id: Integer. unique identifier for each tweet. Older tweets have smaller IDs.

• tweet_created: Timestamp. Time of the tweet's creation.

• tweet_extracted: Timestamp. The UTC time when the ETL pipeline pulled the tweet and its metadata (likes count, retweets count, etc).

• text: String. The raw payload text from the tweet.

• lang: String. Short name for the Tweet text's language.

• user_id: Integer. Twitter's unique user id.

• user_name: String. The author's public name on Twitter.

• user_username: String. The author's Twitter account username (@example)

• user_location: String. The author's public location.

• user_description: String. The author's public profile's bio.

• user_created: Timestamp. Timestamp of user's Twitter account creation.

• user_followers_count: Integer. The number of followers of the author's account at the moment of the tweet extraction

• user_following_count: Integer. The number of followed accounts from the author's account at the moment of the Tweet extraction

• user_tweet_count: Integer. The number of Tweets that the author has published at the moment of the Tweet extraction.

• user_verified: Boolean. True if the user is verified (blue mark).

• source: The device/app used to publish the tweet (Apparently not working, all values are Nan so far).

• retweet_count: Integer. Number of retweets to the Tweet at the moment of the Tweet extraction.

• like_count: Integer. Number of Likes to the Tweet at the moment of the Tweet extraction.

• reply_count: Integer. Number of reply messages to the Tweet.

• impression_count: Integer. Number of times the Tweet has been seen at the moment of the Tweet extraction.

More info: Tweets API info definition: https://developer.twitter.com/en/docs/twitter-api/data-dictionary/object-model/tweet Users API info definition: https://developer.twitter.com/en/docs/twitter-api/data-dictionary/object-model/user

X Premium subscription service to X (formerly Twitter) costing users ***** U.S. dollars per month. X Premium allows users to add a blue checkmark to their account, which was once a feature only given to verified profiles. The service also offers access to premium features, such as the ability to edit published tweets. As of April 2023, it was reported that there were around *** thousand X Premium subscribers.

We present GeoCoV19, a large-scale Twitter dataset related to the ongoing COVID-19 pandemic. The dataset has been collected over a period of 90 days from February 1 to May 1, 2020 and consists of more than 524 million multilingual tweets. As the geolocation information is essential for many tasks such as disease tracking and surveillance, we employed a gazetteer-based approach to extract toponyms from user location and tweet content to derive their geolocation information using the Nominatim (Open Street Maps) data at different geolocation granularity levels. In terms of geographical coverage, the dataset spans over 218 countries and 47K cities in the world. The tweets in the dataset are from more than 43 million Twitter users, including around 209K verified accounts. These users posted tweets in 62 different languages.

Twitter has been a prominent forum for academics communicating online, both among themselves and with policymakers or the broader public. Elon Musk’s take-over of the company brought sweeping change to many aspects of the platform, including the public access to its data; Twitter’s approach to censorship and mis/disinformation; and tweaks to the affordances of the platform. In this letter, we take up a narrower empirical question: what did Elon Musk’s takeover of the platform mean for this academic ecosystem? Using a snowball sample of more than 15,700 academic accounts from the fields of economics, political science, sociology, and psychology, we show that academics in these fields reduced their “engagement” with the platform, measured with either the number of active accounts (i.e., those registering any behavior on a given day) or the number of tweets written (including original tweets, replies, retweets, or quote tweets). We further test whether this drop-off in engagement differed by account type, finding that verified users were significantly more likely to reduce their production of content (i.e., writing new tweets or quoting others’ tweets), but not their engagement with the platform writ large (i.e., retweeting or replying to others’ content).

This dataset contains 50,000 tweets related to job vacancies and hiring, extracted using the keywords 'Job Vacancy,' 'We are Hiring,' and 'We're Hiring'. The tweets were collected between January 1, 2019, and April 10, 2023, with the help of snscrape library of Python and are provided in a CSV format.

The purpose behind this dataset

• To explore text pre-processing and test NLP skills
• Draw interesting insights on Job Market from Job Postings.
• Analyse company/role requirements if possible

The dataset includes the following information for each tweet: ID: The unique identifier for the tweet. Timestamp: The date and time when the tweet was posted. User: The Twitter handle of the user who posted the tweet. Text: The content of the tweet. Hashtag: The hashtags included in the tweet, if any. Retweets: The number of times the tweet has been retweeted as of the time it was scraped. Likes: The number of likes the tweet has received as of the time it was scraped. Replies: The number of replies to the tweet as of the time it was scraped. Source: The source application or device used to post the tweet. Location: The location listed on the user's Twitter profile, if any. Verified_Account: A Boolean value indicating whether the user's Twitter account has been verified. Followers: The number of followers the user has as of the time the tweet was scraped. Following: The number of accounts the user is following as of the time the tweet was scraped

Databases of highly networked individuals have been indispensable in studying narratives and influence on social media. To support studies on Twitter in India, we present a systematically categorized database of accounts of influence on Twitter in India, identified and annotated through an iterative process of friends, networks, and self-described profile information, verified manually. We built an initial set of accounts based on the friend network of a seed set of accounts based on real-world renown in various fields, and then snowballed friends of friends\" multiple times, and rank ordered individuals based on the number of in-group connections, and overall followers. We then manually classified identified accounts under the categories of entertainment, sports, business, government, institutions, journalism, civil society accounts that have independent standing outside of social media, as well as a category ofdigital first" referring to accounts that derive their primary influence from online activity. Overall, we annotated 11580 unique accounts across all categories. The database is useful studying various questions related to the role of influencers in polarisation, misinformation, extreme speech, political discourse etc.

This corpus consists of 46,962 tweets related to the Catalan referendum, a very controversial topic in Spain due to it was an independence referendum called by the Catalan regional government and suspended by the Constitutional Court of Spain after a request from the Spanish government. All the tweets were downloaded on October 1, 2017 with the hashtags #CatalanReferendum or #ReferendumCatalan. Later, we collected features of these tweets on October 31, 2017 in order to analyze their virality. Each item in this collection is made up of the features we used from each tweet to perform the virality analysis: lang: Tweet language. retweet_count: Total number of retweets recorded for a given tweet. favourite_count: Total number of favourites recorded for a given tweet. is_quote_status: Whether a tweet includes a quote of another tweet. num_hashtags: Total number of hashtags in the tweet. num_urls: Total number of URLs in the tweet. num_mentions: Total number of users mentioned in the tweet. interval_time: Interval of the day on which the tweet was published (morning (06:00-12:00), afternoon (12:00-18:00), evening (18:00-00:00) or night (00:00-06:00)). positive_words_iSOL: Total number of positive words found in the tweet using iSOL lexicon. negative_words_iSOL: Total number of negative words found in the tweet using iSOL lexicon. positive_words_NRC: Total number of positive words found in the tweet using NRC lexicon. negative_words_NRC: Total number of negative words found in the tweet using NRC lexicon.
positive_words_mlSenticon: Total number of positive words found in the tweet using ML-SentiCon lexicon. negative_words_mlSenticon: Total number of negative words found in the tweet using ML-SentiCon lexicon. verified_user: Whether the tweet is from a verified user. followers_count_user: Total number of users who follow the author of a tweet. friends_count_user: Total number of friends that the author is following. listed_count_user: Total number of lists that include the author of a tweet. favourites_count_user: Total number of favourited tweets by a user. statuses_count_user: Total number of tweets made by the author since the creation of the account.

Context

Bitcoin(₿) is a cryptocurrency invented in 2008 by an unknown person or group of people using the name Satoshi Nakamoto. The currency began use in 2009 when its implementation was released as open-source software.

Bitcoin is a decentralized digital currency, without a central bank or single administrator, that can be sent from user to user on the peer-to-peer bitcoin network without the need for intermediaries. Transactions are verified by network nodes through cryptography and recorded in a public distributed ledger called a blockchain. Bitcoins are created as a reward for a process known as mining. They can be exchanged for other currencies, products, and services.

On 30 November 2020, bitcoin hit a new all-time high of $19,860 topping the previous high from December 2017. On 19 January 2021 Elon Musk placed #Bitcoin in his Twitter profile tweeting “In retrospect, it was inevitable”, which caused the price to briefly rise about $5000 in an hour to $37,299.

Content

The tweets have #Bitcoin and #btc hashtag.. Collection star started on 6/2/2021, with an initial 100,000 tweets, and will continue on a daily basis.

Information regarding the data

The data totally consists of 1 lakh+ records with 13 columns. The description of the features is given below | No |Columns | Descriptions | | -- | -- | -- | | 1 | user_name | The name of the user, as they’ve defined it. | | 2 | user_location | The user-defined location for this account’s profile. | | 3 | user_description | The user-defined UTF-8 string describing their account. | | 4 | user_created | Time and date, when the account was created. | | 5 | user_followers | The number of followers an account currently has. | | 6 | user_friends | The number of friends an account currently has. | | 7 | user_favourites | The number of favorites an account currently has | | 8 | user_verified | When true, indicates that the user has a verified account | | 9 | date | UTC time and date when the Tweet was created | | 10 | text | The actual UTF-8 text of the Tweet | | 11 | hashtags | All the other hashtags posted in the tweet along with #Bitcoin & #btc | | 12 | source | Utility used to post the Tweet, Tweets from the Twitter website have a source value - web | | 13 | is_retweet | Indicates whether this Tweet has been Retweeted by the authenticating user. |

Inspiration

The tweets were extracted using tweepy, Refer to this notebook for the complete extraction process https://www.kaggle.com/kaushiksuresh147/twitter-data-extraction-for-ipl2020

You can use this data to dive into the subjects that use this hashtag, look to the geographical distribution, evaluate sentiments, looks at trends.

The first public large-scale multilingual Twitter dataset related to the FIFA World Cup 2022, comprising over 28 million posts in 69 unique spoken languages, including Arabic, English, Spanish, French, and many others. This dataset aims to facilitate research in future sentiment analysis, cross-linguistic studies, event-based analytics, meme and hate speech detection, fake news detection, and social manipulation detection.

The file 🚨Qatar22WC.csv🚨 contains tweet-level and user-level metadata for our collected tweets. 🚀Codebook for FIFA World Cup 2022 Twitter Dataset🚀 | Column Name | Description| |-------------------------------- |----------------------------------------------------------------------------------------| | day, month, year | The date where the tweet posted | | hou, min, sec | Hour, minute, and second of tweet timestamp | | age_of_the_user_account | User Account age in days | | tweet_count | Total number of tweets posted by the user | | location | User-defined location field | | follower_count | Number of followers the user has | | following_count | Number of accounts the user is following | | follower_to_Following | Follower-following ratio | | favouite_count | Number of likes the user did| | verified | Boolean indicating if the user is verified (1 = Verified, 0 = Not Verified) | | Avg_tweet_count | Average tweets per day for the user activity| | list_count | Number of lists the user is a member | | Tweet_Id | Tweet ID | | is_reply_tweet | ID of the tweet being replied to (if applicable) | | is_quote | boolean representing if the tweet is a quote | | retid | Retweet ID if it's a retweet; NaN otherwise | | lang | Language of the tweet | | hashtags | The keyword or hashtag used to collect the tweet | | is_image, | Boolean indicating if the tweet associated with image| | is_video | Boolean indicating if the tweet associated with video | |-------------------------------|----------------------------------------------------------------------------------------|

Examples of use case queries are described in the file 🚨fifa_wc_qatar22_examples_of_use_case_queries.ipynb🚨 and accessible via: https://github.com/khairied/Qata_FIFA_World_Cup_22

🚀 Please Cite This as: Daouadi, K. E., Boualleg, Y., Guehairia, O. & Taleb-Ahmed, A. (2025). Tracking the Global Pulse: The first public Twitter dataset from FIFA World Cup, Journal of Computational Social Science.

Context

The following information can also be found at https://www.kaggle.com/davidwallach/financial-tweets. Out of curosity, I just cleaned the .csv files to perform a sentiment analysis. So both the .csv files in this dataset are created by me.

Anything you read in the description is written by David Wallach and using all this information, I happen to perform my first ever sentiment analysis.

"I have been interested in using public sentiment and journalism to gather sentiment profiles on publicly traded companies. I first developed a Python package (https://github.com/dwallach1/Stocker) that scrapes the web for articles written about companies, and then noticed the abundance of overlap with Twitter. I then developed a NodeJS project that I have been running on my RaspberryPi to monitor Twitter for all tweets coming from those mentioned in the content section. If one of them tweeted about a company in the stocks_cleaned.csv file, then it would write the tweet to the database. Currently, the file is only from earlier today, but after about a month or two, I plan to update the tweets.csv file (hopefully closer to 50,000 entries.

I am not quite sure how this dataset will be relevant, but I hope to use these tweets and try to generate some sense of public sentiment score."

Content

This dataset has all the publicly traded companies (tickers and company names) that were used as input to fill the tweets.csv. The influencers whose tweets were monitored were: ['MarketWatch', 'business', 'YahooFinance', 'TechCrunch', 'WSJ', 'Forbes', 'FT', 'TheEconomist', 'nytimes', 'Reuters', 'GerberKawasaki', 'jimcramer', 'TheStreet', 'TheStalwart', 'TruthGundlach', 'Carl_C_Icahn', 'ReformedBroker', 'benbernanke', 'bespokeinvest', 'BespokeCrypto', 'stlouisfed', 'federalreserve', 'GoldmanSachs', 'ianbremmer', 'MorganStanley', 'AswathDamodaran', 'mcuban', 'muddywatersre', 'StockTwits', 'SeanaNSmith'

Acknowledgements

The data used here is gathered from a project I developed : https://github.com/dwallach1/StockerBot

Inspiration

I hope to develop a financial sentiment text classifier that would be able to track Twitter's (and the entire public's) feelings about any publicly traded company (and cryptocurrency)

The global market for social platform account transactions is experiencing robust growth, driven by the increasing popularity of social media platforms and the rising demand for verified accounts and enhanced online presence. This market encompasses the buying and selling of established accounts across various platforms, including Instagram, TikTok, YouTube, and Twitter. The market's expansion is fueled by several key drivers: businesses seeking readily available audiences for marketing and advertising; influencers aiming to boost their follower counts and engagement; and individuals wanting to establish a significant online presence quickly. While precise figures are unavailable, considering a reasonable CAGR (let's assume 15% based on similar rapidly growing digital markets) and a 2025 market size of $500 million (a conservative estimate given the scale of online transactions), we can project substantial growth over the coming years. The market is segmented by application (publicity, sales, education, entertainment, others) and account type (number of followers), offering diverse opportunities for various stakeholders. Competition is relatively fragmented with several key players—both established companies and smaller, region-specific operators—contributing to the market's dynamic nature. However, regulatory challenges and concerns about fraudulent activities pose significant restraints to market growth, necessitating robust verification and security measures. Geographic distribution reveals significant variations in market penetration. North America and Asia-Pacific, with their large and active social media user bases, are expected to dominate the market. However, growth in other regions, especially emerging economies, is poised to accelerate as internet penetration increases and the benefits of verified accounts become more widely understood. The significant historical period growth, projected CAGR and current market players signal an expanding and competitive market landscape, presenting both opportunities and challenges for businesses involved in the social platform account transaction market. Future growth will likely hinge on the ability of companies to address regulatory concerns, enhance security protocols, and adapt to the evolving landscape of social media platforms.

The number of Twitter users in Brazil was forecast to continuously increase between 2024 and 2028 by in total *** million users (+***** percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach ***** million users and therefore a new peak in 2028. Notably, the number of Twitter users of was continuously increasing over the past years.User figures, shown here regarding the platform twitter, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

This project examines how to enhance users' exposure to and engagement with verified and ideologically balanced news in an ecologically valid setting. We rely on a large-scale two-week long field experiment on 28,457 Twitter users. We created 28 bots utilizing GPT-2 that replied to users tweeting about sports, entertainment, or lifestyle with a contextual reply containing two hardcoded elements: a URL to the topic-relevant section of quality news organization and an encouragement to follow its Twitter account. Treated users were randomly assigned to receive responses by bots presented as female or male. We examine whether our intervention enhances the following of news media organization, the sharing/liking of news content and the tweeting/liking of political content. We find that the treated users followed more news accounts and the users in the female bot treatment were more likely to like news content than the control.

This Dataset accompanies:Benigni, Matthew, Joseph, Kenneth, and Carley, Kathleen. n.d. “Online Threat-Group-Supporting Community Detection: Uncovering the ISIS Supporting Community on Twitter.” Under Review Plos One.and can be analyzed using R source code provided at: https://github.com/mbenigni/OSNThreatGroupsThe following files are contained in this dataset:Files:deIdentified_attributes.csv - contains node attribute information for users associated with the 2 hop snowball sample described in the aformantioned work. The file contains the following fields: anonID,followingCount,followerCount,tweetCount,lastTweet,creation_date,lang,suspended,official. AnonID refers to a unique identifier assigned to each user and corresponds to nodes in the provided edge lists. The suspended field refers to accounts that were suspended by Twitter between NOV14 and MAR15. Some of these suspended accounts were used as positive case labels. A full explanation is provided in the article. The official field refers to a list of human verified media, government, and celebrity accounts used to train the 'official classifier' in our presented work. All other fields correspond to fields provided by the Twitter API.deIdentified_friend_edges.csv - a directed network edge list of the following or friend ties associated with all nodes listed in deIdentified_attributes.csv.deIdentified_mention_edges.csv - a directed network edge list of the mention ties associated with all nodes listed in deIdentified_attributes.csv. Additionally epoch time for each edge is provided in the 'date' field.deIdentified_user_ht_edges.csv - a bipartite network edge list of the user to hash tag ties associated with all nodes listed in deIdentified_attributes.csv. Additionally epoch time for each edge is provided in the 'date' field.