The number of Twitter users in the United States was forecast to continuously increase between 2024 and 2028 by in total 4.3 million users (+5.32 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 85.08 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 150 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).Find more key insights for the number of Twitter users in countries like Canada and Mexico.

At the end of October 2022, Elon Musk concluded his acquisition of Twitter. In the weeks and months before that, several questions were publicly discussed that were not only of interest to the platform's future buyers, but also of high relevance to the Computational Social Science research community. For example, how many active users does the platform have? What percentage of accounts on the site are bots? And, what are the dominating topics and sub-topical spheres on the platform? In a globally coordinated effort of 80 scholars to shed light on these questions, and to offer a dataset that will equip other researchers to do the same, we have collected 375 million tweets published within a 24-hour time period starting on September 21, 2022. To the best of our knowledge, this is the first complete 24-hour Twitter dataset that is available for the research community. With it, the present work aims to accomplish two goals. First, we seek to answer the aforementioned questions and provide descriptive metrics about Twitter that can serve as references for other researchers. Second, we create a baseline dataset for future research that can be used to study the potential impact of the platform's ownership change.

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).

Author: Víctor Yeste. Universitat Politècnica de Valencia.The object of this study is the design of a cybermetric methodology whose objectives are to measure the success of the content published in online media and the possible prediction of the selected success variables.In this case, due to the need to integrate data from two separate areas, such as web publishing and the analysis of their shares and related topics on Twitter, has opted for programming as you access both the Google Analytics v4 reporting API and Twitter Standard API, always respecting the limits of these.The website analyzed is hellofriki.com. It is an online media whose primary intention is to solve the need for information on some topics that provide daily a vast number of news in the form of news, as well as the possibility of analysis, reports, interviews, and many other information formats. All these contents are under the scope of the sections of cinema, series, video games, literature, and comics.This dataset has contributed to the elaboration of the PhD Thesis:Yeste Moreno, VM. (2021). Diseño de una metodología cibermétrica de cálculo del éxito para la optimización de contenidos web [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/176009Data have been obtained from each last-minute news article published online according to the indicators described in the doctoral thesis. All related data are stored in a database, divided into the following tables:tesis_followers: User ID list of media account followers.tesis_hometimeline: data from tweets posted by the media account sharing breaking news from the web.status_id: Tweet IDcreated_at: date of publicationtext: content of the tweetpath: URL extracted after processing the shortened URL in textpost_shared: Article ID in WordPress that is being sharedretweet_count: number of retweetsfavorite_count: number of favoritestesis_hometimeline_other: data from tweets posted by the media account that do not share breaking news from the web. Other typologies, automatic Facebook shares, custom tweets without link to an article, etc. With the same fields as tesis_hometimeline.tesis_posts: data of articles published by the web and processed for some analysis.stats_id: Analysis IDpost_id: Article ID in WordPresspost_date: article publication date in WordPresspost_title: title of the articlepath: URL of the article in the middle webtags: Tags ID or WordPress tags related to the articleuniquepageviews: unique page viewsentrancerate: input ratioavgtimeonpage: average visit timeexitrate: output ratiopageviewspersession: page views per sessionadsense_adunitsviewed: number of ads viewed by usersadsense_viewableimpressionpercent: ad display ratioadsense_ctr: ad click ratioadsense_ecpm: estimated ad revenue per 1000 page viewstesis_stats: data from a particular analysis, performed at each published breaking news item. Fields with statistical values can be computed from the data in the other tables, but total and average calculations are saved for faster and easier further processing.id: ID of the analysisphase: phase of the thesis in which analysis has been carried out (right now all are 1)time: "0" if at the time of publication, "1" if 14 days laterstart_date: date and time of measurement on the day of publicationend_date: date and time when the measurement is made 14 days latermain_post_id: ID of the published article to be analysedmain_post_theme: Main section of the published article to analyzesuperheroes_theme: "1" if about superheroes, "0" if nottrailer_theme: "1" if trailer, "0" if notname: empty field, possibility to add a custom name manuallynotes: empty field, possibility to add personalized notes manually, as if some tag has been removed manually for being considered too generic, despite the fact that the editor put itnum_articles: number of articles analysednum_articles_with_traffic: number of articles analysed with traffic (which will be taken into account for traffic analysis)num_articles_with_tw_data: number of articles with data from when they were shared on the media’s Twitter accountnum_terms: number of terms analyzeduniquepageviews_total: total page viewsuniquepageviews_mean: average page viewsentrancerate_mean: average input ratioavgtimeonpage_mean: average duration of visitsexitrate_mean: average output ratiopageviewspersession_mean: average page views per sessiontotal: total of ads viewedadsense_adunitsviewed_mean: average of ads viewedadsense_viewableimpressionpercent_mean: average ad display ratioadsense_ctr_mean: average ad click ratioadsense_ecpm_mean: estimated ad revenue per 1000 page viewsTotal: total incomeretweet_count_mean: average incomefavorite_count_total: total of favoritesfavorite_count_mean: average of favoritesterms_ini_num_tweets: total tweets on the terms on the day of publicationterms_ini_retweet_count_total: total retweets on the terms on the day of publicationterms_ini_retweet_count_mean: average retweets on the terms on the day of publicationterms_ini_favorite_count_total: total of favorites on the terms on the day of publicationterms_ini_favorite_count_mean: average of favorites on the terms on the day of publicationterms_ini_followers_talking_rate: ratio of followers of the media Twitter account who have recently published a tweet talking about the terms on the day of publicationterms_ini_user_num_followers_mean: average followers of users who have spoken of the terms on the day of publicationterms_ini_user_num_tweets_mean: average number of tweets published by users who spoke about the terms on the day of publicationterms_ini_user_age_mean: average age in days of users who have spoken of the terms on the day of publicationterms_ini_ur_inclusion_rate: URL inclusion ratio of tweets talking about terms on the day of publicationterms_end_num_tweets: total tweets on terms 14 days after publicationterms_ini_retweet_count_total: total retweets on terms 14 days after publicationterms_ini_retweet_count_mean: average retweets on terms 14 days after publicationterms_ini_favorite_count_total: total bookmarks on terms 14 days after publicationterms_ini_favorite_count_mean: average of favorites on terms 14 days after publicationterms_ini_followers_talking_rate: ratio of media Twitter account followers who have recently posted a tweet talking about the terms 14 days after publicationterms_ini_user_num_followers_mean: average followers of users who have spoken of the terms 14 days after publicationterms_ini_user_num_tweets_mean: average number of tweets published by users who have spoken about the terms 14 days after publicationterms_ini_user_age_mean: the average age in days of users who have spoken of the terms 14 days after publicationterms_ini_ur_inclusion_rate: URL inclusion ratio of tweets talking about terms 14 days after publication.tesis_terms: data of the terms (tags) related to the processed articles.stats_id: Analysis IDtime: "0" if at the time of publication, "1" if 14 days laterterm_id: Term ID (tag) in WordPressname: Name of the termslug: URL of the termnum_tweets: number of tweetsretweet_count_total: total retweetsretweet_count_mean: average retweetsfavorite_count_total: total of favoritesfavorite_count_mean: average of favoritesfollowers_talking_rate: ratio of followers of the media Twitter account who have recently published a tweet talking about the termuser_num_followers_mean: average followers of users who were talking about the termuser_num_tweets_mean: average number of tweets published by users who were talking about the termuser_age_mean: average age in days of users who were talking about the termurl_inclusion_rate: URL inclusion ratio

The number of Twitter users in France 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).Find more key insights for the number of Twitter users in countries like Luxembourg and Netherlands.

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.

Elon Musk is a prominent American business magnate, known for co-founding PayPal and leading companies such as SpaceX, Tesla, SolarCity, OpenAI, Neuralink, and The Boring Company. He is celebrated for his visionary concepts, considerable media presence, and sharp business acumen. This dataset comprises all tweets posted by his official Twitter handle, @elonmusk, offering a unique opportunity to explore his public opinions on various subjects and to observe how his posting frequency has evolved over time.

Columns (List and describe each column found in the 'Original Data Sample'.)

• Date Created: Indicates the specific date and time when each tweet was published.
• Number of Likes: Represents the total count of likes or positive engagements received by a particular tweet.
• Source of Tweet: Specifies the application or platform from which the tweet was posted, such as "Twitter for iPhone" or "Twitter Web Client."
• Tweets: Contains the full textual content of each tweet.

Distribution (Detail the dataset's format, size, and structure. If specific numbers for rows/records are not available, mention this.)

This dataset is structured in a tabular format, typically presented as a CSV file, making it straightforward to use. It encompasses 17,234 individual tweet entries. The temporal span of the dataset ranges from 6th April 2010 to 12th September 2022. The exact file size in megabytes or gigabytes is not specified.

Usage (List ideal applications and use cases for the dataset.)

This dataset is well-suited for diverse analytical applications, including natural language processing (NLP) to analyse sentiments and themes within Elon Musk's tweets, and trend analysis to monitor changes in his tweet volume or public reception. It is also valuable for social media research, business intelligence investigations, and financial market analysis, particularly for understanding the impact of public figures' statements.

Coverage (Explain the geographic, time range, and demographic scope, including any specific notes on data availability for certain groups/years.)

The dataset covers a substantial period from 6th April 2010 to 12th September 2022. The content consists of tweets from Elon Musk, an individual with a prominent presence in the United States and globally. The data provides a historical record of his public communication through Twitter across this twelve-year span.

License (State the license URL.)

CC0

Who Can Use It (List examples of intended users and their use cases.)

This dataset is intended for researchers studying social media dynamics, analysts performing public sentiment analysis, data scientists building NLP or machine learning models, financial professionals assessing market reactions to public figures, and media scholars investigating the online presence of influential individuals. It is particularly useful for anyone seeking to understand Elon Musk's public discourse and communication style.

Dataset Name Suggestions (Provide several relevant name suggestions for the dataset )

• Elon Musk Twitter Archive
• @elonmusk Public Statements Dataset
• Musk Tweet Activity Analysis
• Elon Musk Social Media Data

Attributes

Original Data Source: Elon Musk Tweets Dataset (17K)

The global number of Twitter users in was forecast to continuously increase between 2024 and 2028 by in total 74.3 million users (+17.32 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 503.42 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 150 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).Find more key insights for the number of Twitter users in countries like South America and the Americas.

We collected the data of a Twitter user using Tweepy to access the Twitter API. We crawled the list of each user account’s followers. Twitter allowed a request of a maximum of 200 tweets per time window and because of limitations of the Twitter API, we could only make a request every 15 minutes. Next, we obtained the most recent tweets of each user in the study. We extracted the most common hashtags used in the sample tweets and crawled the most recent 50 tweets that contained each hashtag and tweets that mentioned a particular user, for example ’@username.’ Initially, we chose 101 user accounts and documented the attributes of each user’s account (number of followers, a list of followers, and the recent tweets of each follower).

The Famous Words Twitter Dataset is a comprehensive collection of tweets associated with famous words. The dataset provides valuable insights into the social media engagement and popularity of these words on the Twitter platform. It includes three primary columns: keyword, likes, and tweets.

The keyword column represents the specific famous word or phrase associated with each tweet. It allows researchers and analysts to explore the dynamics of user interactions and discussions surrounding these popular terms on Twitter.

The likes column indicates the number of likes received by each tweet. This metric serves as an indicator of the tweet's popularity and resonation among Twitter users.

The tweet column contains the actual tweet text, capturing the content and context of user-generated messages related to the famous words. This column provides valuable qualitative data for sentiment analysis, topic modeling, and other natural language processing tasks.

Researchers, data scientists, and social media analysts can leverage this dataset to study various aspects, such as tracking trends, sentiment analysis, understanding user engagement patterns, and identifying influential topics associated with famous words on Twitter.

Topics: "COVID-19", "Vaccine", "Zoom", "Bitcoin", "Dogecoin", "NFT", "Elon Musk", "Tesla", "Amazon", "iPhone 12", "Remote work", "TikTok", "Instagram", "Facebook", "YouTube", "Netflix", "GameStop", "Super Bowl", "Olympics", "Black Lives Matter" "India vs England", "Ukraine", "Queen Elizabeth", "World Cup", "Jeffrey Dahmer", "Johnny Depp", "Will Smith", "Weather", "xvideo", "porn", "nba", "Macdonald",

Total has 128837 tweets, and here are the plot for each number of tweets for different keyword

https://i.imgur.com/z4xbbyt.png" alt="">

Note: The dataset is carefully curated, anonymized, and stripped of any personally identifiable information to protect user privacy.

Twitter is a social news website. It can be viewed as a hybrid of email, instant messaging and sms messaging all rolled into one neat and simple package. It s a new and easy way to discover the latest news related to subjects you care about. |Attribute|Value| |-|-| |Number of Nodes: |11316811| |Number of Edges: |85331846| |Missing Values? |no| |Source:| N/A| ##Data Set Information: 1. nodes.csv — it s the file of all the users. This file works as a dictionary of all the users in this data set. It s useful for fast reference. It contains all the node ids used in the dataset 2. edges.csv — this is the friendship/followership network among the users. The friends/followers are represented using edges. Edges are directed. Here is an example. 1,2 This means user with id "1" is followering user with id "2". ##Attribute Information: Twitter is a social news website. It can be viewed as a hybrid of email, instant messaging and sms messaging all rolled into one ne

Context

Worldwide Social Media User in 2021 (Quarterly)

Acknowledgements

Facebook: https://www.statista.com/statistics/264810/number-of-monthly-active-facebook-users-worldwide/ Twitter: https://investor.twitterinc.com/home/default.aspx Instagram: https://investor.fb.com/home/default.aspx

High Frequency Indicator: The dataset contains year- and month-wise compiled data from the year 2021 to till date on the number of different types of grievances (complaints) received from the users by Twitter and the action taken by it. The data compiled is based on the monthly transparency reports published by Twitter in accordance with Rule 4(1)(d) of the Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021 (IT Rules, 2021).

The types of grievances received by Twitter include illegal activities, IP-related infringements and other issues such as Abuse,Harassment, Child Sexual Exploitation, Defamation, Hateful Conduct, Impersonation, Misinformation, etc. The action taken by Twitter on the basis of these reports includes the number of URLs actioned

This dataset focuses on Twitter engagement metrics related to the Coronavirus disease (COVID-19), an infectious disease caused by the SARS-CoV-2 virus [1]. It provides a detailed collection of tweets, including their text content, the accounts that posted them, any hashtags used, and the geographical locations associated with the accounts [1]. The dataset is valuable for understanding public discourse, information dissemination, and engagement patterns on Twitter concerning COVID-19, particularly for analysing how people experience mild to moderate symptoms and recover, or require medical attention [1].

Columns

• Datetime: Represents the exact date and time a tweet was posted [2].
• Tweet Id: A unique identifier assigned to each tweet [2].
• Text: The actual content of the tweet [2].
• Username: The display name of the tweet author [2].
• Permalink: The direct link to the tweet on Twitter [2].
• User: A link to the author's Twitter account [2].
• Outlinks: Any external links included within the tweet [2].
• CountLinks: The number of links present in the tweet [2].
• ReplyCount: The total number of replies to that specific tweet [2].
• RetweetCount: The total number of retweets of that specific tweet [2].
• DateTime Count: A daily count of tweets, aggregated by date ranges [2].
• Label Count: A count associated with specific ranges of tweet IDs or other engagement metrics, indicating the distribution of tweets within those ranges [3-5].

Distribution

The dataset is structured with daily tweet counts and covers a period from 10 January 2020 to 28 February 2020 [2, 6, 7]. It includes approximately 179,040 daily tweet entries during this timeframe, derived from the sum of daily counts and tweet ID counts [2, 3, 6-11]. Tweet activity shows distinct peaks, with notable increases in late January (e.g., 6,091 tweets between 23-24 January 2020) [2] and a significant surge in late February, reaching 47,643 tweets between 26-27 February 2020, followed by 42,289 and 44,824 in subsequent days [7, 10, 11]. The distribution of certain tweet engagement metrics, such as replies or retweets, indicates that a substantial majority of tweets (over 152,500 records) fall within lower engagement ranges (e.g., 0-43 or 0-1628.96), with fewer tweets showing very high engagement (e.g., only 1 record between 79819.04-81448.00) [4, 5]. The data file would typically be in CSV format [12].

Usage

This dataset is ideal for: * Data Science and Analytics projects focused on social media [1]. * Visualization of tweet trends and engagement over time. * Exploratory data analysis to uncover patterns in COVID-19 related discussions [1]. * Natural Language Processing (NLP) tasks, such as sentiment analysis or topic modelling on tweet content [1]. * Data cleaning and preparation exercises for social media data [1].

Coverage

The dataset has a global geographic scope [13]. It covers tweet data from 10 January 2020 to 28 February 2020 [2, 6, 7]. The content is specific to the Coronavirus disease (COVID-19) [1].

License

CC0

Who Can Use It

This dataset is particularly useful for: * Data scientists and analysts interested in social media trends and public health discourse [1]. * Researchers studying information spread and public sentiment during health crises. * Developers building AI and LLM data solutions [13]. * Individuals interested in exploratory analysis and data visualization of real-world social media data [1].

Dataset Name Suggestions

• COVID-19 Twitter Engagement Data
• SARS-CoV-2 Tweet Activity Log
• Pandemic Social Media Discourse
• Coronavirus Tweets Analytics
• Global COVID-19 Tweet Metrics

Attributes

Original Data Source: Covid_19 Tweets Dataset

The dataset consists of various columns containing information related to tweets posted on Twitter. Each row in the dataset represents a single tweet. Here's an explanation of the columns in the dataset from a third-person perspective:

Tweet: This column contains the actual text content of the tweet. It includes the message that the user posted on Twitter. Tweets can vary in length from a few characters to the maximum allowed by Twitter.

Sentiment: This column indicates the sentiment or emotional tone of the tweet. Sentiment can be classified into categories such as positive, negative, or neutral. It reflects the overall opinion or attitude expressed in the tweet.

Username: This column contains the username of the Twitter account that posted the tweet. Each Twitter user has a unique username that identifies their account.

Timestamp: This column contains the timestamp indicating when the tweet was posted. It includes information about the date and time when the tweet was published on Twitter.

Retweets: This column represents the number of times the tweet has been retweeted by other Twitter users. A retweet is when a user shares another user's tweet with their followers.

Likes: This column indicates the number of likes or favorites received by the tweet. Users can express their appreciation for a tweet by liking it.

Hashtags: This column contains any hashtags included in the tweet. Hashtags are keywords or phrases preceded by the "#" symbol, used to categorize or label tweets and make them more discoverable.

Mentions: This column includes any Twitter usernames mentioned in the tweet. Mentions are when a user tags another user in their tweet by including their username preceded by the "@" symbol.

Location: This column provides information about the location associated with the tweet. It may include details such as the city, state, country, or geographical coordinates from which the tweet was posted, if available.

Source: This column specifies the source or platform used to post the tweet. It indicates whether the tweet was posted from the Twitter website, a mobile app, or a third-party application.

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.

The number of Twitter users in Mexico was forecast to continuously increase between 2024 and 2028 by in total 3.1 million users (+23.7 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 16.21 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 150 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).Find more key insights for the number of Twitter users in countries like Canada and United States.

This is the dataset for the article "A Predictive Method to Improve the Effectiveness of Twitter Communication in a Cultural Heritage Scenario".

Abstract:

Museums are embracing social technologies in the attempt to broaden their audience and to engage people. Although social communication seems an easy task, media managers know how hard it is to reach millions of people with a simple message. Indeed, millions of posts are competing every day to get visibility in terms of likes and shares and very little research focused on museums communication to identify best practices. In this paper, we focus on Twitter and we propose a novel method that exploits interpretable machine learning techniques to: (a) predict whether a tweet will likely be appreciated by Twitter users or not; (b) present simple suggestions that will help enhancing the message and increasing the probability of its success. Using a real-world dataset of around 40,000 tweets written by 23 world famous museums, we show that our proposed method allows identifying tweet features that are more likely to influence the tweet success.

Code to run a selection of experiments is available at https://github.com/rmartoglia/predict-twitter-ch

Dataset structure

The dataset contains the dataset used in the experiments of the above research paper. Only the extracted features for the museum tweet threads (and not the message full text) are provided and needed for the analyses.

We selected 23 well known world spread art museums and grouped them into five groups: G1 (museums with at least three million of followers); G2 (museums with more than one million of followers); G3 (museums with more than 400,000 followers); G4 (museums with more that 200,000 followers); G5 (Italian museums). From these museums, we analyzed ca. 40,000 tweets, with a number varying from 5k ca. to 11k ca. for each museum group, depending on the number of museums in each group.

Content features: these are the features that can be drawn form the content of the tweet itself. We further divide such features in the following two categories:

– Countable: these features have a value ranging into different intervals. We take into consideration: the number of hashtags (i.e., words preceded by #) in the tweet, the number of URLs (i.e., links to external resources), the number of images (e.g., photos and graphical emoticons), the number of mentions (i.e., twitter accounts preceded by @), the length of the tweet;

– On-Off : these features have binary values in {0, 1}. We observe whether the tweet has exclamation marks, question marks, person names, place names, organization names, other names. Moreover, we also take into consideration the tweet topic density: assuming that the involved topics correspond to the hashtags mentioned in the text, we define a tweet as dense of topics if the number of hashtags it contains is greater than a given threshold, set to 5. Finally, we observe the tweet sentiment that might be present (positive or negative) or not (neutral).

Context features: these features are not drawn form the content of the tweet itself and might give a larger picture of the context in which the tweet was sent. Namely, we take into consideration the part of the day in which the tweet was sent (morning, afternoon, evening and night respectively from 5:00am to 11:59am, from 12:00pm to 5:59pm, from 6:00pm to 10:59pm and from 11pm to 4:59am), and a boolean feature indicating whether the tweet is a retweet or not.

User features: these features are proper of the user that sent the tweet, and are the same for all the tweets of this user. Namely we consider the name of the museum and the number of followers of the user.

This dataset comprises a set of Twitter accounts in Singapore that are used for social bot profiling research conducted by the Living Analytics Research Centre (LARC) at Singapore Management University (SMU). Here a bot is defined as a Twitter account that generates contents and/or interacts with other users automatically (at least according to human judgment). In this research, Twitter bots have been categorized into three major types:

Broadcast bot. This bot aims at disseminating information to general audience by providing, e.g., benign links to news, blogs or sites. Such bot is often managed by an organization or a group of people (e.g., bloggers). Consumption bot. The main purpose of this bot is to aggregate contents from various sources and/or provide update services (e.g., horoscope reading, weather update) for personal consumption or use. Spam bot. This type of bots posts malicious contents (e.g., to trick people by hijacking certain account or redirecting them to malicious sites), or promotes harmless but invalid/irrelevant contents aggressively.

This categorization is general enough to cater for new, emerging types of bot (e.g., chatbots can be viewed as a special type of broadcast bots). The dataset was collected from 1 January to 30 April 2014 via the Twitter REST and streaming APIs. Starting from popular seed users (i.e., users having many followers), their follow, retweet, and user mention links were crawled. The data collection proceeds by adding those followers/followees, retweet sources, and mentioned users who state Singapore in their profile location. Using this procedure, a total of 159,724 accounts have been collected. To identify bots, the first step is to check active accounts who tweeted at least 15 times within the month of April 2014. These accounts were then manually checked and labelled, of which 589 bots were found. As many more human users are expected in the Twitter population, the remaining accounts were randomly sampled and manually checked. With this, 1,024 human accounts were identified. In total, this results in 1,613 labelled accounts. Related Publication: R. J. Oentaryo, A. Murdopo, P. K. Prasetyo, and E.-P. Lim. (2016). On profiling bots in social media. Proceedings of the International Conference on Social Informatics (SocInfo’16), 92-109. Bellevue, WA. https://doi.org/10.1007/978-3-319-47880-7_6

SSH CENTRE (Social Sciences and Humanities for Climate, Energy aNd Transport Research Excellence) is a Horizon Europe project, engaging directly with stakeholders across research, policy, and business (including citizens) to strengthen social innovation, SSH-STEM collaboration, transdisciplinary policy advice, inclusive engagement, and SSH communities across Europe, accelerating the EU’s transition to carbon neutrality. SSH CENTRE is based in a range of activities related to Open Science, inclusivity and diversity – especially with regards Southern and Eastern Europe and different career stages – including: development of novel SSH-STEM collaborations to facilitate the delivery of the EU Green Deal; SSH knowledge brokerage to support regions in transition; and the effective design of strategies for citizen engagement in EU R&I activities. Outputs include action-led agendas and building stakeholder synergies through regular Policy Insight events.This is captured in a high-profile virtual SSH CENTRE generating and sharing best practice for SSH policy advice, overcoming fragmentation to accelerate the EU’s journey to a sustainable future.The documents uploaded here are part of WP2 whereby novel, interdisciplinary teams were provided funding to undertake activities to develop a policy recommendation related to EU Green Deal policy. Each of these policy recommendations, and the activities that inform them, will be written-up as a chapter in an edited book collection. Three books will make up this edited collection - one on climate, one on energy and one on mobility. As part of writing a chapter for the SSH CENTRE book on ‘Mobility’, we set out to analyse the sentiment of users on Twitter regarding shared and active mobility modes in Brussels. This involved us collecting tweets between 2017-2022. A tweet was collected if it contained a previously defined mobility keyword (for example: metro) and either the name of a (local) politician, a neighbourhood or municipality, or a (shared) mobility provider. The files attached to this Zenodo webpage is a csv files containing the tweets collected.”.