This dataset provides detailed rankings and key metrics for 100+ social media platforms and sites in 2025. It includes information such as user base, popularity trends, and global reach. Ideal for analyzing social media growth, user engagement, and market trends. Whether you're a data scientist, marketer, or researcher, this dataset offers valuable insights into the evolving digital landscape.

Gain valuable insights with our comprehensive Social Media Dataset, designed to help businesses, marketers, and analysts track trends, monitor engagement, and optimize strategies. This dataset provides structured and reliable social media data from multiple platforms.

Dataset Features

User Profiles: Access public social media profiles, including usernames, bios, follower counts, engagement metrics, and more. Ideal for audience analysis, influencer marketing, and competitive research. Posts & Content: Extract posts, captions, hashtags, media (images/videos), timestamps, and engagement metrics such as likes, shares, and comments. Useful for trend analysis, sentiment tracking, and content strategy optimization. Comments & Interactions: Analyze user interactions, including replies, mentions, and discussions. This data helps brands understand audience sentiment and engagement patterns. Hashtag & Trend Tracking: Monitor trending hashtags, topics, and viral content across platforms to stay ahead of industry trends and consumer interests.

Customizable Subsets for Specific Needs Our Social Media Dataset is fully customizable, allowing you to filter data based on platform, region, keywords, engagement levels, or specific user profiles. Whether you need a broad dataset for market research or a focused subset for brand monitoring, we tailor the dataset to your needs.

Popular Use Cases

Brand Monitoring & Reputation Management: Track brand mentions, customer feedback, and sentiment analysis to manage online reputation effectively. Influencer Marketing & Audience Analysis: Identify key influencers, analyze engagement metrics, and optimize influencer partnerships. Competitive Intelligence: Monitor competitor activity, content performance, and audience engagement to refine marketing strategies. Market Research & Consumer Insights: Analyze social media trends, customer preferences, and emerging topics to inform business decisions. AI & Predictive Analytics: Leverage structured social media data for AI-driven trend forecasting, sentiment analysis, and automated content recommendations.

Whether you're tracking brand sentiment, analyzing audience engagement, or monitoring industry trends, our Social Media Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

This database is comprised of 951 participants who provided self-report data online in their school classrooms. The data was collected in 2016 and 2017. The dataset is comprised of 509 males (54%) and 442 females (46%). Their ages ranged from 12 to 16 years (M = 13.69, SD = 0.72). Seven participants did not report their age. The majority were born in Australia (N = 849, 89%). The next most common countries of birth were China (N = 24, 2.5%), the UK (N = 23, 2.4%), and the USA (N = 9, 0.9%). Data were drawn from students at five Australian independent secondary schools. The data contains item responses for the Spence Children’s Anxiety Scale (SCAS; Spence, 1998) which is comprised of 44 items. The Social media question asked about frequency of use with the question “How often do you use social media?”. The response options ranged from constantly to once a week or less. Items measuring Fear of Missing Out were included and incorporated the following five questions based on the APS Stress and Wellbeing in Australia Survey (APS, 2015). These were “When I have a good time it is important for me to share the details online; I am afraid that I will miss out on something if I don’t stay connected to my online social networks; I feel worried and uncomfortable when I can’t access my social media accounts; I find it difficult to relax or sleep after spending time on social networking sites; I feel my brain burnout with the constant connectivity of social media. Internal consistency for this measure was α = .81. Self compassion was measured using the 12-item short-form of the Self-Compassion Scale (SCS-SF; Raes et al., 2011). The data set has the option of downloading an excel file (composed of two worksheet tabs) or CSV files 1) Data and 2) Variable labels. References: Australian Psychological Society. (2015). Stress and wellbeing in Australia survey. https://www.headsup.org.au/docs/default-source/default-document-library/stress-and-wellbeing-in-australia-report.pdf?sfvrsn=7f08274d_4 Raes, F., Pommier, E., Neff, K. D., & Van Gucht, D. (2011). Construction and factorial validation of a short form of the self-compassion scale. Clinical Psychology and Psychotherapy, 18(3), 250-255. https://doi.org/10.1002/cpp.702 Spence, S. H. (1998). A measure of anxiety symptoms among children. Behaviour Research and Therapy, 36(5), 545-566. https://doi.org/10.1016/S0005-7967(98)00034-5

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.

These datasets contain public comments on social media from the three-month period specifically tagged with the keywords "breastfeeding" and "formula milk". The datasets are mainly about infant feeding, allowing us to do exploratory data analysis from the public point of view.

About Content

status_id : Numerical assigned id which is unique

created_at : Posted day-time

text : Posted text (character base)

display_ text_ width : Length of the comment (number of characters)

country : Country of the post

day : Posted the day of the week

What can be done?

This data is suitable for text mining with sentiment analysis. There are some examples below:

• Which feeding method has more audience than the other? • What are the top 10 popular words for each feeding method? • Does the country parameter influence sentiment on infant feeding strategies? • Is there a particular situation (like a special week of organisations) for the increasing trend of the topic? • Which day of the week do people share positive comments mostly?

Important : its a 3 month gap data Starting from March 2022 to Dec 2022

Influencers are categorized by the number of followers they have on social media. They include celebrities with large followings to niche content creators with a loyal following on social-media platforms such as YouTube, Instagram, Facebook, and Twitter.Their followers range in number from hundreds of millions to 1,000. Influencers may be categorized in tiers (mega-, macro-, micro-, and nano-influencers), based on their number of followers.

Businesses pursue people who aim to lessen their consumption of advertisements, and are willing to pay their influencers more. Targeting influencers is seen as increasing marketing's reach, counteracting a growing tendency by prospective customers to ignore marketing.

Marketing researchers Kapitan and Silvera find that influencer selection extends into product personality. This product and benefit matching is key. For a shampoo, it should use an influencer with good hair. Likewise, a flashy product may use bold colors to convey its brand. If an influencer is not flashy, they will clash with the brand. Matching an influencer with the product's purpose and mood is important.

https://sceptermarketing.com/wp-content/uploads/2019/02/social-media-influencers-2l4ues9.png">

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.

This dataset contains metadata about all Covid-related YouTube videos which circulated on public social media, but which YouTube eventually removed because they contained false information. It describes 8,122 videos that were shared between November 2019 and June 2020. The dataset contains unique identifiers for the videos and social media accounts that shared the videos, statistics on social media engagement and metadata such as video titles and view counts where they were recoverable. We publish the data alongside the code used to produce on Github. The dataset has reuse potential for research studying narratives related to the coronavirus, the impact of social media on knowledge about health and the politics of social media platforms.

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.

More than 100 social media channels and statistics for the National Archives and Records Administration.

The report provides a snapshot of the social media usage trends amongst online Canadian adults based on an online survey of 1500 participants. Canada continues to be one of the most connected countries in the world. An overwhelming majority of online Canadian adults (94%) have an account on at least one social media platform. However, the 2022 survey results show that the COVID-19 pandemic has ushered in some changes in how and where Canadians are spending their time on social media. Dominant platforms such as Facebook, messaging apps and YouTube are still on top but are losing ground to newer platforms such as TikTok and more niche platforms such as Reddit and Twitch.

Same News Different Sources

How different sources report on the same events

About this dataset

Do you ever feel like you're being inundated with news from all sides, and you can't keep up? Well, you're not alone. In today's age of social media and 24-hour news cycles, it can be difficult to know what's going on in the world. And with so many different news sources to choose from, it can be hard to know who to trust.

That's where this dataset comes in. It captures data related to individuals' Sentiment Analysis toward different news sources. The data was collected by administering a survey to individuals who use different news sources. The survey responses were then analyzed to obtain the sentiment score for each news source.

So if you're feeling overwhelmed by the news, don't worry – this dataset has you covered. With its insights on which news sources are trustworthy and which ones aren't, you'll be able to make informed decisions about what to read – and what to skip

How to use the dataset

The Twitter Sentiment Analysis dataset can be used to analyze the impact of social media on news consumption. This data can be used to study how individuals' sentiments towards different news sources vary based on the source they use. The dataset can also be used to study how different factors, such as the time of day or the topic of the news, affect an individual's sentiments

Research Ideas

• Identify which news sources are most trusted by the public.
• Understand what topics are most important to the public.
• Understand how different news sources report on the same issue

Columns

File: news.csv | Column name | Description | |:-----------------------|:------------------------------------------------------| | **** | | | Title | The title of the news article. (String) | | Date | The date the news article was published. (Date) | | Time | The time the news article was published. (Time) | | Score | The sentiment score of the news article. (Float) | | Number of Comments | The number of comments on the news article. (Integer) |

File: news_api.csv | Column name | Description | |:--------------|:------------------------------------------------| | **** | | | Title | The title of the news article. (String) | | Date | The date the news article was published. (Date) | | Source | The news source the article is from. (String) |

File: politics.csv | Column name | Description | |:-----------------------|:------------------------------------------------------| | **** | | | Title | The title of the news article. (String) | | Date | The date the news article was published. (Date) | | Time | The time the news article was published. (Time) | | Score | The sentiment score of the news article. (Float) | | Number of Comments | The number of comments on the news article. (Integer) |

File: sports.csv | Column name | Description | |:-----------------------|:------------------------------------------------------| | **** | | | Title | The title of the news article. (String) | | Date | The date the news article was published. (Date) | | Time | The time the news article was published. (Time) | | Score | The sentiment score of the news article. (Float) | | Number of Comments | The number of comments on the news article. (Integer) |

File: television.csv | Column name | Description | |:-----------------------|:------------------------------------------------------| | **** | | | Title | The title of the news article. (String) | | Date | The date the news article was published. (Date) | | Time | The time the news article was published. (Time) | | Score | The sentiment score of the news article. (Float) | | Number of Comments | The number of comments on the news article. (Integer) |

File: trending.csv | Column name | Description ...

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.

Social behavior has a fundamental impact on the dynamics of infectious diseases (such as COVID-19), challenging public health mitigation strategies and possibly the political consensus. The widespread use of the traditional and social media on the Internet provides us with an invaluable source of information on societal dynamics during pandemics. With this dataset, we aim to understand mechanisms of COVID-19 epidemic-related social behavior in Poland deploying methods of computational social science and digital epidemiology. We have collected and analyzed COVID-19 perception on the Polish language Internet during 15.01-31.07(06.08) and labeled data quantitatively (Twitter, Youtube, Articles) and qualitatively (Facebook, Articles and Comments of Article) in the Internet by infomediological approach.

• manually labelled1,449 articles / Facebook posts from Lower Silesia (facebook_articles_lower_silesia.zip) and 111 texts from outside this region;

-manually labelled 1000 most popular tweets (twits_annotated.xlsx) with cathegories is_fake (categorical and numeric) topic and sentiment;

-extracted 57,306 representative articles (articles_till_06_08.zip) in Polish using Eventregitry.org tool in language Polish and topic "Coronavirus" in article body;

• extracted 1,015,199 (tweets_till_31_07_users.zip and tweets_till_31_07_text.zip) and Tweets from #Koronawirus in language Polish using Twitter API.

• collected 1,574 videos (youtube_comments_till_31_07.zip and youtube_movie.csv) with keyword: Koronawirus on YouTube and 247,575 comments on them using Google API;

• We supplemented the media observations with an analysis of 244 social empirical studies till 25.05 on COVID-19 in Poland (empirical_social_studies.csv).

Reports and analyzes and coding books can be found in Polish at: http://www.infodemia-koronawirusa.pl

Main report (in Polish) https://depot.ceon.pl/handle/123456789/19215

This dataset covers aspects of online politics in 25 democracies: 15 relatively old established European democracies (Austria, Belgium, Denmark, Finland, France, Germany, Iceland, Ireland, Italy, Luxembourg, Netherlands, Norway, Sweden, Switzerland, United Kingdom); five non-European veteran democracies (Australia, Canada, Israel, Japan, New Zealand); two early (Portugal, Spain) and three late (Czech Republic, Hungary, Poland) third-wave (young) European democracies. The research population includes, in each country, parties that won 4% or more of the votes in two consecutive elections before April 2019 (a total of 141 parties and 145 leaders). The dataset includes external party level information such as performance in the last national elections, governmental status, party age, populism affiliation and leadership selection method. It also includes information related to the party leaders such as their term in leadership office and other formal positions. In addition it includes information about online activity mainly on the consumption (user related activities) of the parties and their leaders in Facebook and Twitter two of the most used social media platforms for political purposes.

Analysis of ‘Which Social Media Millennials Care About Most?’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/which-social-media-millennials-care-about-moste on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

This data was collected by Whatsgoodly, a millennial social polling company.

It was published by Brietbart on 3/17/17.

Link to article here: http://www.breitbart.com/tech/2017/03/17/report-snapchat-is-most-important-social-network-among-millennials/

This dataset was created by Adam Halper and contains around 500 samples along with Segment Type, Count, technical information and other features such as: - Segment Description - Answer - and more.

How to use this dataset

• Analyze Percentage in relation to Question
• Study the influence of Segment Type on Count
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Adam Halper

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

MuMiN is a misinformation graph dataset containing rich social media data (tweets, replies, users, images, articles, hashtags), spanning 21 million tweets belonging to 26 thousand Twitter threads, each of which have been semantically linked to 13 thousand fact-checked claims across dozens of topics, events and domains, in 41 different languages, spanning more than a decade.

MuMiN fills a gap in the existing misinformation datasets in multiple ways:

By having a large amount of social media information which have been semantically linked to fact-checked claims on an individual basis. By featuring 41 languages, enabling evaluation of multilingual misinformation detection models. By featuring both tweets, articles, images, social connections and hashtags, enabling multimodal approaches to misinformation detection.

MuMiN features two node classification tasks, related to the veracity of a claim:

Claim classification: Determine the veracity of a claim, given its social network context. Tweet classification: Determine the likelihood that a social media post to be fact-checked is discussing a misleading claim, given its social network context.

To use the dataset, see the "Getting Started" guide and tutorial at the MuMiN website.

This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/

The goal of this dataset is to predict sentiment score for news title. This dataset contains 83164 time series obtained from the News Popularity in Multiple Social Media Platforms dataset from the UCI repository. This is a large data set of news items and their respective social feedback on multiple platforms: Facebook, Google+ and LinkedIn. The collected data relates to a period of 8 months, between November 2015 and July 2016, accounting for about 100,000 news items on four different topics: economy, microsoft, obama and palestine. This data set is tailored for evaluative comparisons in predictive analytics tasks, although allowing for tasks in other research areas such as topic detection and tracking, sentiment analysis in short text, first story detection or news recommendation. The time series has 3 dimensions.

Please refer to https://archive.ics.uci.edu/ml/datasets/News+Popularity+in+Multiple+Social+Media+Platforms for more details

Citation request
Nuno Moniz and Luis Torgo (2018), Multi-Source Social Feedback of Online News Feeds, CoRR

The results might surprise you when looking at internet users that are active on social media in each country.

56.8% of the world’s total population is active on social media.