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.

The undertaking of several studies of political phenomena in social media mandates the operationalization of the notion of political stance of users and contents involved. Relevant examples include the study of segregation and polarization online, the study of political diversity in content diets in social media, or AI explainability. While many research designs rely on operationalizations best suited for the US setting, few allow addressing more general design, in which users and content might take stances on multiple ideology and issue dimensions, going beyond traditional Liberal-Conservative or Left-Right scales. To advance the study of more general online ecosystems, we present a dataset of X/Twitter population of users in the French political Twittersphere and web domains embedded in a political space spanned by dimensions measuring attitudes towards immigration, the EU, liberal values, elites and institutions, nationalism and the environment. We provide several benchmarks validating the positions of these entities (based on both, LLM and human annotations), and discuss several applications for this dataset.

Dataset Description

Paper: TBC Point of Contact: Josh McGiff (Josh.McGiff@ul.ie)

Dataset Summary This dataset was developed to address the significant gap in online hate speech detection, particularly focusing on homophobia, which is often neglected in sentiment analysis research. It comprises tweets scraped from X (formerly Twitter), which have been labeled for the presence of homophobic content by volunteers from diverse backgrounds. This dataset is the largest open-source labelled English dataset for homophobia detection known to the authors and aims to enhance online safety and inclusivity.

Supported Tasks

Task: Homophobic hate speech detection.

Languages English.

Dataset Structure

Data Fields: tweet_text: The text content of the tweet. label: Binary label indicating the presence of homophobic content (0 = no homophobic content, 1 = homophobic content). 'language': The language of the tweet, as tagged by X/Twitter.

Dataset Creation

Curation Rationale: The dataset was curated to enhance the detection and classification of homophobic content on social media platforms, particularly focusing on the gap where homophobia is underrepresented in current research. Source Data: Data was scraped from X (formerly Twitter) focusing on terms and accounts associated with the LGBTQIA+ community. Annotation Process: Annotations were made by three volunteers from different sexualities and gender identities using a majority vote for label assignment. Annotations were conducted in Microsoft Excel over several days. Personal and Sensitive Information: Usernames and other personal identifiers have been anonymized or removed. URLs have also been removed. The dataset contains sensitive content related to homophobia.

Considerations for Using the Data

Social Impact: The dataset is intended for research purposes to combat online hate speech and improve inclusivity and safety on digital platforms. Ethical Considerations: Given the sensitive nature of hate speech, researchers should consider the impact of their work on marginalised communities and ensure that their use of the dataset aims to reduce harm and promote inclusivity. Legal and Privacy Concerns: Researchers should comply with legal standards and ethical guidelines regarding hate speech and data privacy.

Additional Information

License: CC-BY-4.0 Citation: TBC

Acknowledgements This work was conducted with the financial support of the Science Foundation Ireland Centre for Research Training in Artificial Intelligence under Grant No. 18/CRT/6223.

*** Fake News on Twitter ***

These 5 datasets are the results of an empirical study on the spreading process of newly fake news on Twitter. Particularly, we have focused on those fake news which have given rise to a truth spreading simultaneously against them. The story of each fake news is as follow:

1- FN1: A Muslim waitress refused to seat a church group at a restaurant, claiming "religious freedom" allowed her to do so.

2- FN2: Actor Denzel Washington said electing President Trump saved the U.S. from becoming an "Orwellian police state."

3- FN3: Joy Behar of "The View" sent a crass tweet about a fatal fire in Trump Tower.

4- FN4: The animated children's program 'VeggieTales' introduced a cannabis character in August 2018.

5- FN5: In September 2018, the University of Alabama football program ended its uniform contract with Nike, in response to Nike's endorsement deal with Colin Kaepernick.

The data collection has been done in two stages that each provided a new dataset: 1- attaining Dataset of Diffusion (DD) that includes information of fake news/truth tweets and retweets 2- Query of neighbors for spreaders of tweets that provides us with Dataset of Graph (DG).

DD

DD for each fake news story is an excel file, named FNx_DD where x is the number of fake news, and has the following structure:

The structure of excel files for each dataset is as follow:

Each row belongs to one captured tweet/retweet related to the rumor, and each column of the dataset presents a specific information about the tweet/retweet. These columns from left to right present the following information about the tweet/retweet:

User ID (user who has posted the current tweet/retweet)

The description sentence in the profile of the user who has published the tweet/retweet

The number of published tweet/retweet by the user at the time of posting the current tweet/retweet

Date and time of creation of the account by which the current tweet/retweet has been posted

Language of the tweet/retweet

Number of followers

Number of followings (friends)

Date and time of posting the current tweet/retweet

Number of like (favorite) the current tweet had been acquired before crawling it

Number of times the current tweet had been retweeted before crawling it

Is there any other tweet inside of the current tweet/retweet (for example this happens when the current tweet is a quote or reply or retweet)

The source (OS) of device by which the current tweet/retweet was posted

Tweet/Retweet ID

Retweet ID (if the post is a retweet then this feature gives the ID of the tweet that is retweeted by the current post)

Quote ID (if the post is a quote then this feature gives the ID of the tweet that is quoted by the current post)

Reply ID (if the post is a reply then this feature gives the ID of the tweet that is replied by the current post)

Frequency of tweet occurrences which means the number of times the current tweet is repeated in the dataset (for example the number of times that a tweet exists in the dataset in the form of retweet posted by others)

State of the tweet which can be one of the following forms (achieved by an agreement between the annotators):

r : The tweet/retweet is a fake news post

a : The tweet/retweet is a truth post

q : The tweet/retweet is a question about the fake news, however neither confirm nor deny it

n : The tweet/retweet is not related to the fake news (even though it contains the queries related to the rumor, but does not refer to the given fake news)

DG

DG for each fake news contains two files:

A file in graph format (.graph) which includes the information of graph such as who is linked to whom. (This file named FNx_DG.graph, where x is the number of fake news)

A file in Jsonl format (.jsonl) which includes the real user IDs of nodes in the graph file. (This file named FNx_Labels.jsonl, where x is the number of fake news)

Because in the graph file, the label of each node is the number of its entrance in the graph. For example if node with user ID 12345637 be the first node which has been entered into the graph file then its label in the graph is 0 and its real ID (12345637) would be at the row number 1 (because the row number 0 belongs to column labels) in the jsonl file and so on other node IDs would be at the next rows of the file (each row corresponds to 1 user id). Therefore, if we want to know for example what the user id of node 200 (labeled 200 in the graph) is, then in jsonl file we should look at row number 202.

The user IDs of spreaders in DG (those who have had a post in DD) would be available in DD to get extra information about them and their tweet/retweet. The other user IDs in DG are the neighbors of these spreaders and might not exist in DD.

The number of Reddit users in the United States was forecast to continuously increase between 2024 and 2028 by in total 10.3 million users (+5.21 percent). After the ninth consecutive increasing year, the Reddit user base is estimated to reach 208.12 million users and therefore a new peak in 2028. Notably, the number of Reddit users of was continuously increasing over the past years.User figures, shown here with regards to the platform reddit, 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 and count multiple accounts by persons only once. Reddit users encompass both users that are logged in and those that are not.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 Reddit users in countries like Mexico and Canada.

The S3 dataset contains the behavior (sensors, statistics of applications, and voice) of 21 volunteers interacting with their smartphones for more than 60 days. The type of users is diverse, males and females in the age range from 18 until 70 have been considered in the dataset generation. The wide range of age is a key aspect, due to the impact of age in terms of smartphone usage. To generate the dataset the volunteers installed a prototype of the smartphone application in on their Android mobile phones.
All attributes of the different kinds of data are writed in a vector. The dataset contains the fellow vectors:
Sensors:
This type of vector contains data belonging to smartphone sensors (accelerometer and gyroscope) that has been acquired in a given windows of time. Each vector is obtained every 20 seconds, and the monitored features are:- Average of accelerometer and gyroscope values.- Maximum and minimum of accelerometer and gyroscope values.- Variance of accelerometer and gyroscope values.- Peak-to-peak (max-min) of X, Y, Z coordinates.- Magnitude for gyroscope and accelerometer.

Statistics:
These vectors contain data about the different applications used by the user recently. Each vector of statistics is calculated every 60 seconds and contains : - Foreground application counters (number of different and total apps) for the last minute and the last day.- Most common app ID and the number of usages in the last minute and the last day. - ID of the currently active app. - ID of the last active app prior to the current one.- ID of the application most frequently utilized prior to the current application. - Bytes transmitted and received through the network interfaces.

Voice:
This kind of vector is generated when the microphone is active in a call o voice note. The speaker vector is an embedding, extracted from the audio, and it contains information about the user's identity. This vector, is usually named "x-vector" in the Speaker Recognition field, and it is calculated following the steps detailed in "egs/sitw/v2" for the Kaldi library, with the models available for the extraction of the embedding.


A summary of the details of the collected database.
- Users: 21 - Sensors vectors: 417.128 - Statistics app's usage vectors: 151.034 - Speaker vectors: 2.720 - Call recordings: 629 - Voice messages: 2.091

Trigger warnings are placed at the beginning of potentially distressing content to provide individuals with the opportunity to avoid the content before exposure. Social media platforms use artificial intelligence to add automatic trigger warnings to certain images and videos, but are less commonly applied to textual content. This leaves the responsibility of adding trigger warnings to the authors, and a failure to do so may expose vulnerable users to sensitive or upsetting content. Due to limited research attention, there is a lack of understanding concerning what content is or is not considered triggering by social media users. To address this gap, we examine the use of trigger warnings in tweets on X, previously known as Twitter. We used a large language model (LLM) for zero-shot learning to identify the types of trigger warnings (e.g., violence, abuse) used in tweets and their prevalence. Additionally, we employed sentiment and emotion analysis to examine each trigger warning category, aiming to identify prevalent emotions and overall sentiment. Two datasets were collected: 48,168 tweets with explicit trigger warnings and 4,980,466 tweets with potentially triggering content. The analysis of the smaller dataset indicates that users have applied trigger warnings more frequently over the years and are applying them to a broader range of content categories than they did in the past. These findings may reflect users’ growing interest in creating a safe space and a supportive online community that is aware of diverse sensitivities among users. Despite these findings, our analysis of the larger dataset confirms a lack of trigger warnings in most potentially triggering content.

As of April 2024, almost 32 percent of global Instagram audiences were aged between 18 and 24 years, and 30.6 percent of users were aged between 25 and 34 years. Overall, 16 percent of users belonged to the 35 to 44 year age group. Instagram users With roughly one billion monthly active users, Instagram belongs to the most popular social networks worldwide. The social photo sharing app is especially popular in India and in the United States, which have respectively 362.9 million and 169.7 million Instagram users each. Instagram features One of the most popular features of Instagram is Stories. Users can post photos and videos to their Stories stream and the content is live for others to view for 24 hours before it disappears. In January 2019, the company reported that there were 500 million daily active Instagram Stories users. Instagram Stories directly competes with Snapchat, another photo sharing app that initially became famous due to it’s “vanishing photos” feature. As of the second quarter of 2021, Snapchat had 293 million daily active users.

Trigger warnings are placed at the beginning of potentially distressing content to provide individuals with the opportunity to avoid the content before exposure. Social media platforms use artificial intelligence to add automatic trigger warnings to certain images and videos, but are less commonly applied to textual content. This leaves the responsibility of adding trigger warnings to the authors, and a failure to do so may expose vulnerable users to sensitive or upsetting content. Due to limited research attention, there is a lack of understanding concerning what content is or is not considered triggering by social media users. To address this gap, we examine the use of trigger warnings in tweets on X, previously known as Twitter. We used a large language model (LLM) for zero-shot learning to identify the types of trigger warnings (e.g., violence, abuse) used in tweets and their prevalence. Additionally, we employed sentiment and emotion analysis to examine each trigger warning category, aiming to identify prevalent emotions and overall sentiment. Two datasets were collected: 48,168 tweets with explicit trigger warnings and 4,980,466 tweets with potentially triggering content. The analysis of the smaller dataset indicates that users have applied trigger warnings more frequently over the years and are applying them to a broader range of content categories than they did in the past. These findings may reflect users’ growing interest in creating a safe space and a supportive online community that is aware of diverse sensitivities among users. Despite these findings, our analysis of the larger dataset confirms a lack of trigger warnings in most potentially triggering content.

The number of LinkedIn users in the United Kingdom was forecast to continuously increase between 2024 and 2028 by in total 1.5 million users (+4.51 percent). After the eighth consecutive increasing year, the LinkedIn user base is estimated to reach 34.7 million users and therefore a new peak in 2028. User figures, shown here with regards to the platform LinkedIn, 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 and count multiple accounts by persons only once.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).

The number of Instagram users in the United Kingdom was forecast to continuously increase between 2024 and 2028 by in total 2.1 million users (+7.02 percent). After the ninth consecutive increasing year, the Instagram user base is estimated to reach 32 million users and therefore a new peak in 2028. Notably, the number of Instagram users of was continuously increasing over the past years.User figures, shown here with regards to the platform instagram, 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 and count multiple accounts by persons only once.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).

The number of Pinterest users in the United Kingdom was forecast to continuously increase between 2024 and 2028 by in total 0.3 million users (+3.14 percent). After the ninth consecutive increasing year, the Pinterest user base is estimated to reach 9.88 million users and therefore a new peak in 2028. Notably, the number of Pinterest users of was continuously increasing over the past years.User figures, shown here regarding the platform pinterest, 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 and count multiple accounts by persons only once.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).