As of April 2024, around 16.5 percent of global active Instagram users were men between the ages of 18 and 24 years. More than half of the global Instagram population worldwide was aged 34 years or younger.

              Teens and social media

              As one of the biggest social networks worldwide, Instagram is especially popular with teenagers. As of fall 2020, the photo-sharing app ranked third in terms of preferred social network among teenagers in the United States, second to Snapchat and TikTok. Instagram was one of the most influential advertising channels among female Gen Z users when making purchasing decisions. Teens report feeling more confident, popular, and better about themselves when using social media, and less lonely, depressed and anxious.
              Social media can have negative effects on teens, which is also much more pronounced on those with low emotional well-being. It was found that 35 percent of teenagers with low social-emotional well-being reported to have experienced cyber bullying when using social media, while in comparison only five percent of teenagers with high social-emotional well-being stated the same. As such, social media can have a big impact on already fragile states of mind.

Percentage of Internet users who have experienced selected personal effects in their life because of the Internet and the use of social networking websites or apps, during the past 12 months.

We analyzed 700 million words, phrases, and topic instances collected from the Facebook messages of 75,000 volunteers, who also took standard personality tests, and found striking variations in language with personality, gender, and age. In our open-vocabulary technique, the data itself drives a comprehensive exploration of language that distinguishes people, finding connections that are not captured with traditional closed-vocabulary word-category analyses. Our analyses shed new light on psychosocial processes yielding results that are face valid (e.g., subjects living in high elevations talk about the mountains), tie in with other research (e.g., neurotic people disproportionately use the phrase ‘sick of’ and the word ‘depressed’), suggest new hypotheses (e.g., an active life implies emotional stability), and give detailed insights (males use the possessive ‘my’ when mentioning their ‘wife’ or ‘girlfriend’ more often than females use ‘my’ with ‘husband’ or 'boyfriend’). To date, this represents the largest study, by an order of magnitude, of language and personality.

The below link provides the full reproducibility package for the WBWP "Using Social Media to Change Gender Norms : An Experiment within Facebook Messenger in India" (10199, Oct 2022). Both the intervention and the data collection for this randomized control trial were delivered through Facebook Messenger using the open source research platform Virtual Lab. The study collected a baseline and two follow up surveys (1 week and 4 months after exposure on average). The research team also measured intervention compliance data (e.g., if study participants clicked and how many seconds they watched study videos, clicked on website links provided at end of the endline survey). The link includes de-identified data for raw, processed, analysis and outputs.

https://www.dropbox.com/sh/nhymmvlwvyjt2x7/AACmAawA2HdMBxeFRSYRy_OYa?dl=0

As of January 2024, Instagram was slightly more popular with men than women, with men accounting for 50.6 percent of the platform’s global users. Additionally, the social media app was most popular amongst younger audiences, with almost 32 percent of users aged between 18 and 24 years.

              Instagram’s Global Audience

              As of January 2024, Instagram was the fourth most popular social media platform globally, reaching two billion monthly active users (MAU). This number is projected to keep growing with no signs of slowing down, which is not a surprise as the global online social penetration rate across all regions is constantly increasing.
              As of January 2024, the country with the largest Instagram audience was India with 362.9 million users, followed by the United States with 169.7 million users.

              Who is winning over the generations?

              Even though Instagram’s audience is almost twice the size of TikTok’s on a global scale, TikTok has shown itself to be a fierce competitor, particularly amongst younger audiences. TikTok was the most downloaded mobile app globally in 2022, generating 672 million downloads. As of 2022, Generation Z in the United States spent more time on TikTok than on Instagram monthly.

Title: Pew Research Center – American Trends Panel Wave 28 Fieldwork Dates: August 8–21, 2017 Sample Size: N = 4,971 U.S. adults Mode: Web-based survey (English and Spanish) Purpose: This wave explores public attitudes on gender equality, gender traits, and social values. It supports multiple Pew reports and Fact Tank posts, including longitudinal analysis with Wave 29. The dataset includes open-ended responses, corrected survey items, and specialized weights for social media and combined-wave analysis.

As of April 2024, it was found that men between the ages of 25 and 34 years made up Facebook largest audience, accounting for 18.4 percent of global users. Additionally, Facebook's second largest audience base could be found with men aged 18 to 24 years.

              Facebook connects the world

              Founded in 2004 and going public in 2012, Facebook is one of the biggest internet companies in the world with influence that goes beyond social media. It is widely considered as one of the Big Four tech companies, along with Google, Apple, and Amazon (all together known under the acronym GAFA). Facebook is the most popular social network worldwide and the company also owns three other billion-user properties: mobile messaging apps WhatsApp and Facebook Messenger,
              as well as photo-sharing app Instagram. Facebook usersThe vast majority of Facebook users connect to the social network via mobile devices. This is unsurprising, as Facebook has many users in mobile-first online markets. Currently, India ranks first in terms of Facebook audience size with 378 million users. The United States, Brazil, and Indonesia also all have more than 100 million Facebook users each.

This dataset contains the raw data obtained from social media interactions (Twitter, Facebook, Instagram and Reddit) among citizens about citizen participation in science and research with social impact related to two Sustainable Development Goals: Quality Education and Gender Equality. The data collection has followed a twofold strategy 1) Top-Down, in which researchers identified and selected relevant Twitter and Instagram hashtags and Facebook and Reddit pages and 2) Bottom-Up, in which Twitter hashtags were selected based on daily Trending Topics.

The data was collected between March 9^th and March 16^th 2021 and has been obtained, cleaned and anonymized following ALLINTERACT Protocol for Social Media Analytics. This dataset is part of EC Horizon 2020 project ALLINTERACT Widening and diversifying citizen engagement in science (872396).

We provide five Excel files (one for each social network explored). Each file contains the main information of the extracted messages, however the information extracted in each case is slightly different.

1. Twitter: Row ID, Tweet ID, Tweet, Time, Tweet Type, Retweeted By, Number of Retweets, Hashtags, Number of Tweets , Number of Followers, Number Following
2. Facebook: Row ID, Post ID, Post, Link, Link Name, Link Caption, Link Description, Video, Type, Likes, Created Time, Updated Time, Comment ID, Comment Text, Comment Likes, Comment Time, Page Likes
3. Instagram: Url, content, likes, comments, date
4. Reddit: Row ID, sub_id, sub_title, sub_text, sub_score, sub_date, sub_link, comment_id, comment_body, comment_score, comment_date, comment_link

Funding: We acknowledge support of this work by the project "ALLINTERACT Widening and diversifying citizen engagement in science” (872396) from the European Commission Horizon 2020 programme.

This data set was used to train a CrowdFlower AI gender predictor. You can read all about the project here. Contributors were asked to simply view a Twitter profile and judge whether the user was a male, a female, or a brand (non-individual). The dataset contains 20,000 rows, each with a user name, a random tweet, account profile and image, location, and even link and sidebar color.

Inspiration

Here are a few questions you might try to answer with this dataset:

• how well do words in tweets and profiles predict user gender?
• what are the words that strongly predict male or female gender?
• how well do stylistic factors (like link color and sidebar color) predict user gender?

Acknowledgments

Data was provided by the Data For Everyone Library on Crowdflower.

Our Data for Everyone library is a collection of our favorite open data jobs that have come through our platform. They're available free of charge for the community, forever.

The Data

The dataset contains the following fields:

• _unit_id: a unique id for user
• _golden: whether the user was included in the gold standard for the model; TRUE or FALSE
• _unit_state: state of the observation; one of finalized (for contributor-judged) or golden (for gold standard observations)
• _trusted_judgments: number of trusted judgments (int); always 3 for non-golden, and what may be a unique id for gold standard observations
• _last_judgment_at: date and time of last contributor judgment; blank for gold standard observations
• gender: one of male, female, or brand (for non-human profiles)
• gender:confidence: a float representing confidence in the provided gender
• profile_yn: "no" here seems to mean that the profile was meant to be part of the dataset but was not available when contributors went to judge it
• profile_yn:confidence: confidence in the existence/non-existence of the profile
• created: date and time when the profile was created
• description: the user's profile description
• fav_number: number of tweets the user has favorited
• gender_gold: if the profile is golden, what is the gender?
• link_color: the link color on the profile, as a hex value
• name: the user's name
• profile_yn_gold: whether the profile y/n value is golden
• profileimage: a link to the profile image
• retweet_count: number of times the user has retweeted (or possibly, been retweeted)
• sidebar_color: color of the profile sidebar, as a hex value
• text: text of a random one of the user's tweets
• tweet_coord: if the user has location turned on, the coordinates as a string with the format "[*latitude*, longitude]"
• tweet_count: number of tweets that the user has posted
• tweet_created: when the random tweet (in the text column) was created
• tweet_id: the tweet id of the random tweet
• tweet_location: location of the tweet; seems to not be particularly normalized
• user_timezone: the timezone of the user

How many people use social media?

              Social media usage is one of the most popular online activities. In 2024, over five billion people were using social media worldwide, a number projected to increase to over six billion in 2028.

              Who uses social media?
              Social networking is one of the most popular digital activities worldwide and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at 59 percent. This figure is anticipated to grow as lesser developed digital markets catch up with other regions
              when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. Mobile-first market Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe.

              How much time do people spend on social media?
              Social media is an integral part of daily internet usage. On average, internet users spend 151 minutes per day on social media and messaging apps, an increase of 40 minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media.

              What are the most popular social media platforms?
              Market leader Facebook was the first social network to surpass one billion registered accounts and currently boasts approximately 2.9 billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.

Facebook received 73,390 user data requests from federal agencies and courts in the United States during the second half of 2023. The social network produced some user data in 88.84 percent of requests from U.S. federal authorities. The United States accounts for the largest share of Facebook user data requests worldwide.

The prevalence of bias in the news media has become a critical issue, affecting public perception on a range of important topics such as political views, health, insurance, resource distributions, religion, race, age, gender, occupation, and climate change. The media has a moral responsibility to ensure accurate information dissemination and to increase awareness about important issues and the potential risks associated with them. This highlights the need for a solution that can help mitigate against the spread of false or misleading information and restore public trust in the media.Data description: This is a dataset for news media bias covering different dimensions of the biases: political, hate speech, political, toxicity, sexism, ageism, gender identity, gender discrimination, race/ethnicity, climate change, occupation, spirituality, which makes it a unique contribution. The dataset used for this project does not contain any personally identifiable information (PII).The data structure is tabulated as follows:Text: The main content.Dimension: Descriptive category of the text.Biased_Words: A compilation of words regarded as biased.Aspect: Specific sub-topic within the main content.Label: Indicates the presence (True) or absence (False) of bias. The label is ternary - highly biased, slightly biased and neutralToxicity: Indicates the presence (True) or absence (False) of bias.Identity_mention: Mention of any identity based on words match.Annotation SchemeThe labels and annotations in the dataset are generated through a system of Active Learning, cycling through:Manual LabelingSemi-Supervised LearningHuman VerificationThe scheme comprises:Bias Label: Specifies the degree of bias (e.g., no bias, mild, or strong).Words/Phrases Level Biases: Pinpoints specific biased terms or phrases.Subjective Bias (Aspect): Highlights biases pertinent to content dimensions.Due to the nuances of semantic match algorithms, certain labels such as 'identity' and 'aspect' may appear distinctively different.List of datasets used : We curated different news categories like Climate crisis news summaries , occupational, spiritual/faith/ general using RSS to capture different dimensions of the news media biases. The annotation is performed using active learning to label the sentence (either neural/ slightly biased/ highly biased) and to pick biased words from the news.We also utilize publicly available data from the following links. Our Attribution to others.MBIC (media bias): Spinde, Timo, Lada Rudnitckaia, Kanishka Sinha, Felix Hamborg, Bela Gipp, and Karsten Donnay. "MBIC--A Media Bias Annotation Dataset Including Annotator Characteristics." arXiv preprint arXiv:2105.11910 (2021). https://zenodo.org/records/4474336Hyperpartisan news: Kiesel, Johannes, Maria Mestre, Rishabh Shukla, Emmanuel Vincent, Payam Adineh, David Corney, Benno Stein, and Martin Potthast. "Semeval-2019 task 4: Hyperpartisan news detection." In Proceedings of the 13th International Workshop on Semantic Evaluation, pp. 829-839. 2019. https://huggingface.co/datasets/hyperpartisan_news_detectionToxic comment classification: Adams, C.J., Jeffrey Sorensen, Julia Elliott, Lucas Dixon, Mark McDonald, Nithum, and Will Cukierski. 2017. "Toxic Comment Classification Challenge." Kaggle. https://kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge.Jigsaw Unintended Bias: Adams, C.J., Daniel Borkan, Inversion, Jeffrey Sorensen, Lucas Dixon, Lucy Vasserman, and Nithum. 2019. "Jigsaw Unintended Bias in Toxicity Classification." Kaggle. https://kaggle.com/competitions/jigsaw-unintended-bias-in-toxicity-classification.Age Bias : Díaz, Mark, Isaac Johnson, Amanda Lazar, Anne Marie Piper, and Darren Gergle. "Addressing age-related bias in sentiment analysis." In Proceedings of the 2018 chi conference on human factors in computing systems, pp. 1-14. 2018. Age Bias Training and Testing Data - Age Bias and Sentiment Analysis Dataverse (harvard.edu)Multi-dimensional news Ukraine: Färber, Michael, Victoria Burkard, Adam Jatowt, and Sora Lim. "A multidimensional dataset based on crowdsourcing for analyzing and detecting news bias." In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 3007-3014. 2020. https://zenodo.org/records/3885351#.ZF0KoxHMLtVSocial biases: Sap, Maarten, Saadia Gabriel, Lianhui Qin, Dan Jurafsky, Noah A. Smith, and Yejin Choi. "Social bias frames: Reasoning about social and power implications of language." arXiv preprint arXiv:1911.03891 (2019). https://maartensap.com/social-bias-frames/Goal of this dataset :We want to offer open and free access to dataset, ensuring a wide reach to researchers and AI practitioners across the world. The dataset should be user-friendly to use and uploading and accessing data should be straightforward, to facilitate usage.If you use this dataset, please cite us.Navigating News Narratives: A Media Bias Analysis Dataset © 2023 by Shaina Raza, Vector Institute is licensed under CC BY-NC 4.0

The global social media penetration rate in was forecast to continuously increase between 2024 and 2028 by in total 11.6 (+18.19 percent). After the ninth consecutive increasing year, the penetration rate is estimated to reach 75.31 and therefore a new peak in 2028. Notably, the social media penetration rate of was continuously increasing over the past years.

A random sample of 20 York University students were surveyed. Two questions were asked: 1) for biological sex, and 2) whether or not they have opened an Instagram account, a social network sharing photos and videos through the Instagram app on mobile devices. Of the 20 students, 19 were asked inside Lumbers, a building with laboratories for multiple scientific disciplines.In the collected data, "Gender" refers to biological sex of the participant. F stands for female and M stands for male. The second variable, "Opened an Instagram Account" records if the participant has ever registered for an Instagram account. The response to the second question was only "yes" or "no".

During a January 2024 global survey among marketers, nearly 60 percent reported plans to increase their organic use of YouTube for marketing purposes in the following 12 months. LinkedIn and Instagram followed, respectively mentioned by 57 and 56 percent of the respondents intending to use them more. According to the same survey, Facebook was the most important social media platform for marketers worldwide.

This dataset is part of EC Horizon 2020 project ALLINTERACT Widening and diversifying citizen engagement in science (872396). It contains the raw data obtained from the fieldwork, which consists of: 1) Literature Review, 2) Social Media Analytics, 3) Focus Groups, 4) Survey and 5) Social Media Communicative Observation. 1) Literature Review The objective of the literature review was to address the following topics in gender and education: a) How citizens’ benefit from scientific research, b) Citizen awareness of the impact of scientific research, c) Awareness-raising initiatives succeeding at engaging citizens in scientific participation, including the Open Access movement and citizen science initiatives, d) Awareness-raising actions that foster the recruitment of new talent in sciences and e) Policies that promote awareness-raising actions and citizen engagement in science. In order to do so, the searches were carried out in the top scientific databases, namely Web of Science (mainly in those journals indexed in Journal Citation Reports) and Scopus. The articles were published between 2010-2021 in journals indexed Q1 or Q2 in JCR or in Q1 journals indexed in Scopus. Relevant reports from EU-funded research projects and official EU documents were also included. We provide one word file with the following information of each topic (a-e) in gender and education. - Keywords used - Criteria of selection - Identified sources - Outcomes - Annexes: Grids with the details of the identified socurces 2) Social Media Analytic It is the raw data obtained from social media interactions (Twitter, Facebook, Instagram and Reddit) among citizens about citizen participation in science and research with social impact related to two Sustainable Development Goals: Quality Education and Gender Equality. The data collection followed a twofold strategy 1) Top-Down, in which researchers identified and selected relevant Twitter and Instagram hashtags and Facebook and Reddit pages and 2) Bottom-Up, in which Twitter hashtags were selected based on daily Trending Topics. The data was collected between March 9th and March 16th 2021 and has been obtained, cleaned and anonymized following Allinteract - Social Media Analytics Protocol (Flecha & Pulido, 2021). We provide five Excel files (one for each social network explored). Each file contains the main information of the extracted messages, however the information extracted in each case is slightly different. -Twitter: Tweet ID, Time, Tweet Type, Retweeted By, Number of Retweets, Hashtags -Facebook: Post ID, Video, Type, Likes, Created Time, Updated Time, Comment ID, Comment Likes, Comment Time, Page Likes -Instagram: Likes, comments, date -Reddit: Row ID, sub_id, sub_title, sub_score, sub_date, comment_id, comment_score, comment_date 3) Focus Groups This data file contains the pseudonymized transcription of a total of 6 focus groups in gender and 6 in education, which were conducted between October 2021 and February 2022. These focus groups are the pre-test and therefore, the groups are distributed in control group or experimental group. The participants of the gender focus groups were women (including vulnerable women) from a women’s group, members of an LGBTQI group and women (including young women) from a women’s group. The participants of the education focus groups were parents, teachers and students. We provide a word file with the literal transcriptions of the focus groups in the language in which the focus groups were conducted (English, Spanish or Portuguese). 4) Survey This data file contains the anonym answers of the survey conducted with participants from 12 countries, through a CATI/CAWI method. The survey was conducted between November 2021 and February 2022 and consists of 59 questions. The exploitation of this data has been carried out with the SPSS software. We provide an excel file with the 59 questions and the answers of 7507 participants. 5) Social Media Communicative Observation The Social Media Communicative Observation aims to explore the effects of introducing scientific pieces of evidence in social media interactions as an initiative to increase participation through awareness. In order to do so, scientific evidence on gender and education were introduced in 10 Facebook groups (5 related to gender and 5 to education), 10 Reddit communities (5 related to gender and 5 to education) and 2 Social Impact Platforms (Sappho and Adhyayana). We provide an excel file with the anonymized interactions among users around the introduced piece of evidence. This Excel file contains the following information: Group of documents, document name, code, start, final, weight, segment, changed by, changed, created, comment, area and percentage (%). 6) Focus Group – Post test This data file contains the pseudonymized transcription of a total of 6 focus groups post test Funding: We acknowledge support of this work by the project "ALLINTERACT Widening and diversifying

This data set contains information on the number of visits and new visitors to the NYC HOPE website (https://www1.nyc.gov/nychope/site/page/home). The website provides information on domestic and gender-based violence, including resources and services that are available in New York City.

If you use the dataset, cite the paper: https://doi.org/10.1016/j.eswa.2022.117541

The most comprehensive dataset to date regarding climate change and human opinions via Twitter. It has the heftiest temporal coverage, spanning over 13 years, includes over 15 million tweets spatially distributed across the world, and provides the geolocation of most tweets. Seven dimensions of information are tied to each tweet, namely geolocation, user gender, climate change stance and sentiment, aggressiveness, deviations from historic temperature, and topic modeling, while accompanied by environmental disaster events information. These dimensions were produced by testing and evaluating a plethora of state-of-the-art machine learning algorithms and methods, both supervised and unsupervised, including BERT, RNN, LSTM, CNN, SVM, Naive Bayes, VADER, Textblob, Flair, and LDA.

The following columns are in the dataset:

➡ created_at: The timestamp of the tweet. ➡ id: The unique id of the tweet. ➡ lng: The longitude the tweet was written. ➡ lat: The latitude the tweet was written. ➡ topic: Categorization of the tweet in one of ten topics namely, seriousness of gas emissions, importance of human intervention, global stance, significance of pollution awareness events, weather extremes, impact of resource overconsumption, Donald Trump versus science, ideological positions on global warming, politics, and undefined. ➡ sentiment: A score on a continuous scale. This scale ranges from -1 to 1 with values closer to 1 being translated to positive sentiment, values closer to -1 representing a negative sentiment while values close to 0 depicting no sentiment or being neutral. ➡ stance: That is if the tweet supports the belief of man-made climate change (believer), if the tweet does not believe in man-made climate change (denier), and if the tweet neither supports nor refuses the belief of man-made climate change (neutral). ➡ gender: Whether the user that made the tweet is male, female, or undefined. ➡ temperature_avg: The temperature deviation in Celsius and relative to the January 1951-December 1980 average at the time and place the tweet was written. ➡ aggressiveness: That is if the tweet contains aggressive language or not.

Since Twitter forbids making public the text of the tweets, in order to retrieve it you need to do a process called hydrating. Tools such as Twarc or Hydrator can be used to hydrate tweets.

The global number of Facebook users was forecast to continuously increase between 2023 and 2027 by in total 391 million users (+14.36 percent). After the fourth consecutive increasing year, the Facebook user base is estimated to reach 3.1 billion users and therefore a new peak in 2027. Notably, the number of Facebook users was continuously increasing over the past years. User figures, shown here regarding the platform Facebook, 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).