71 datasets found
  1. Instagram accounts with the most followers worldwide 2024

    • statista.com
    • davegsmith.com
    Updated Jun 17, 2025
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    Stacy Jo Dixon (2025). Instagram accounts with the most followers worldwide 2024 [Dataset]. https://www.statista.com/topics/1164/social-networks/
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    Dataset updated
    Jun 17, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Stacy Jo Dixon
    Description

    Cristiano Ronaldo has one of the most popular Instagram accounts as of April 2024.

                  The Portuguese footballer is the most-followed person on the photo sharing app platform with 628 million followers. Instagram's own account was ranked first with roughly 672 million followers.
    
                  How popular is Instagram?
    
                  Instagram is a photo-sharing social networking service that enables users to take pictures and edit them with filters. The platform allows users to post and share their images online and directly with their friends and followers on the social network. The cross-platform app reached one billion monthly active users in mid-2018. In 2020, there were over 114 million Instagram users in the United States and experts project this figure to surpass 127 million users in 2023.
    
                  Who uses Instagram?
    
                  Instagram audiences are predominantly young – recent data states that almost 60 percent of U.S. Instagram users are aged 34 years or younger. Fall 2020 data reveals that Instagram is also one of the most popular social media for teens and one of the social networks with the biggest reach among teens in the United States.
    
                  Celebrity influencers on Instagram
                  Many celebrities and athletes are brand spokespeople and generate additional income with social media advertising and sponsored content. Unsurprisingly, Ronaldo ranked first again, as the average media value of one of his Instagram posts was 985,441 U.S. dollars.
    
  2. What social Media People like the most and why?

    • kaggle.com
    Updated Feb 17, 2023
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    Nina Luquez (2023). What social Media People like the most and why? [Dataset]. https://www.kaggle.com/ninaluquez/what-social-media-people-like-the-most-and-why/discussion
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Feb 17, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Nina Luquez
    Description

    Dataset

    This dataset was created by Nina Luquez

    Contents

  3. Instagram: distribution of global audiences 2024, by gender

    • statista.com
    • davegsmith.com
    Updated Jun 17, 2025
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    Stacy Jo Dixon (2025). Instagram: distribution of global audiences 2024, by gender [Dataset]. https://www.statista.com/topics/1164/social-networks/
    Explore at:
    Dataset updated
    Jun 17, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Stacy Jo Dixon
    Description

    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.
    
  4. Instagram: most used hashtags 2024

    • statista.com
    • davegsmith.com
    Updated Jun 17, 2025
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    Statista Research Department (2025). Instagram: most used hashtags 2024 [Dataset]. https://www.statista.com/topics/1164/social-networks/
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    Dataset updated
    Jun 17, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Description

    As of January 2024, #love was the most used hashtag on Instagram, being included in over two billion posts on the social media platform. #Instagood and #instagram were used over one billion times as of early 2024.

  5. Social Media Profile Links by Name

    • openwebninja.com
    json
    Updated Feb 2, 2025
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    OpenWeb Ninja (2025). Social Media Profile Links by Name [Dataset]. https://www.openwebninja.com/api/social-links-search
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    jsonAvailable download formats
    Dataset updated
    Feb 2, 2025
    Dataset authored and provided by
    OpenWeb Ninja
    Area covered
    Worldwide
    Description

    This dataset provides comprehensive social media profile links discovered through real-time web search. It includes profiles from major social networks like Facebook, TikTok, Instagram, Twitter, LinkedIn, Youtube, Pinterest, Github and more. The data is gathered through intelligent search algorithms and pattern matching. Users can leverage this dataset for social media research, influencer discovery, social presence analysis, and social media marketing. The API enables efficient discovery of social profiles across multiple platforms. The dataset is delivered in a JSON format via REST API.

  6. m

    Abbreviated FOMO and social media dataset

    • figshare.mq.edu.au
    • researchdata.edu.au
    txt
    Updated May 30, 2023
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    Danielle Einstein; Carol Dabb; Madeleine Ferrari; Anne McMaugh; Peter McEvoy; Ron Rapee; Eyal Karin; Maree J. Abbott (2023). Abbreviated FOMO and social media dataset [Dataset]. http://doi.org/10.25949/20188298.v1
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    txtAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    Macquarie University
    Authors
    Danielle Einstein; Carol Dabb; Madeleine Ferrari; Anne McMaugh; Peter McEvoy; Ron Rapee; Eyal Karin; Maree J. Abbott
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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

  7. Instagram: distribution of global audiences 2024, by age group

    • statista.com
    • davegsmith.com
    Updated Jun 17, 2025
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    Stacy Jo Dixon (2025). Instagram: distribution of global audiences 2024, by age group [Dataset]. https://www.statista.com/topics/1164/social-networks/
    Explore at:
    Dataset updated
    Jun 17, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Stacy Jo Dixon
    Description

    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.
    
  8. Data from: Youtube social network

    • kaggle.com
    zip
    Updated Sep 1, 2019
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    Lorenzo De Tomasi (2019). Youtube social network [Dataset]. https://www.kaggle.com/lodetomasi1995/youtube-social-network
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    zip(10604317 bytes)Available download formats
    Dataset updated
    Sep 1, 2019
    Authors
    Lorenzo De Tomasi
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Area covered
    YouTube
    Description

    Youtube social network and ground-truth communities Dataset information Youtube is a video-sharing web site that includes a social network. In the Youtube social network, users form friendship each other and users can create groups which other users can join. We consider such user-defined groups as ground-truth communities. This data is provided by Alan Mislove et al.

    We regard each connected component in a group as a separate ground-truth community. We remove the ground-truth communities which have less than 3 nodes. We also provide the top 5,000 communities with highest quality which are described in our paper. As for the network, we provide the largest connected component.

    more info : https://snap.stanford.edu/data/com-Youtube.html

  9. Instagram: distribution of global audiences 2024, by age and gender

    • statista.com
    • davegsmith.com
    Updated Jun 17, 2025
    + more versions
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    Stacy Jo Dixon (2025). Instagram: distribution of global audiences 2024, by age and gender [Dataset]. https://www.statista.com/topics/1164/social-networks/
    Explore at:
    Dataset updated
    Jun 17, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Stacy Jo Dixon
    Description

    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.
    
  10. P

    TikTok Dataset Dataset

    • paperswithcode.com
    Updated Jul 22, 2024
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    Yasamin Jafarian; Hyun Soo Park (2024). TikTok Dataset Dataset [Dataset]. https://paperswithcode.com/dataset/tiktok-dataset
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    Dataset updated
    Jul 22, 2024
    Authors
    Yasamin Jafarian; Hyun Soo Park
    Description

    We learn high fidelity human depths by leveraging a collection of social media dance videos scraped from the TikTok mobile social networking application. It is by far one of the most popular video sharing applications across generations, which include short videos (10-15 seconds) of diverse dance challenges as shown above. We manually find more than 300 dance videos that capture a single person performing dance moves from TikTok dance challenge compilations for each month, variety, type of dances, which are moderate movements that do not generate excessive motion blur. For each video, we extract RGB images at 30 frame per second, resulting in more than 100K images. We segmented these images using Removebg application, and computed the UV coordinates from DensePose.

    Download TikTok Dataset:

    Please use the dataset only for the research purpose.

    The dataset can be viewed and downloaded from the Kaggle page. (you need to make an account in Kaggle to be able to download the data. It is free!)

    The dataset can also be downloaded from here (42 GB). The dataset resolution is: (1080 x 604)

    The original YouTube videos corresponding to each sequence and the dance name can be downloaded from here (2.6 GB).

  11. O

    State of Oregon Social Media Sites

    • data.oregon.gov
    • catalog.data.gov
    • +2more
    application/rdfxml +5
    Updated Feb 16, 2024
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    Department of Administrative Services (2024). State of Oregon Social Media Sites [Dataset]. https://data.oregon.gov/Administrative/State-of-Oregon-Social-Media-Sites/hqhe-shsc
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    csv, xml, json, tsv, application/rssxml, application/rdfxmlAvailable download formats
    Dataset updated
    Feb 16, 2024
    Dataset authored and provided by
    Department of Administrative Services
    License

    https://www.usa.gov/government-workshttps://www.usa.gov/government-works

    Area covered
    Oregon
    Description

    List of social media sites by agency

  12. f

    Data from: Mpox Narrative on Instagram: A Labeled Multilingual Dataset of...

    • figshare.com
    xlsx
    Updated Oct 12, 2024
    + more versions
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    Nirmalya Thakur (2024). Mpox Narrative on Instagram: A Labeled Multilingual Dataset of Instagram Posts on Mpox for Sentiment, Hate Speech, and Anxiety Analysis [Dataset]. http://doi.org/10.6084/m9.figshare.27072247.v1
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    Oct 12, 2024
    Dataset provided by
    figshare
    Authors
    Nirmalya Thakur
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Please cite this paper when using this dataset: N. Thakur, “Mpox narrative on Instagram: A labeled multilingual dataset of Instagram posts on mpox for sentiment, hate speech, and anxiety analysis,” arXiv [cs.LG], 2024, URL: https://arxiv.org/abs/2409.05292Abstract: The world is currently experiencing an outbreak of mpox, which has been declared a Public Health Emergency of International Concern by WHO. During recent virus outbreaks, social media platforms have played a crucial role in keeping the global population informed and updated regarding various aspects of the outbreaks. As a result, in the last few years, researchers from different disciplines have focused on the development of social media datasets focusing on different virus outbreaks. No prior work in this field has focused on the development of a dataset of Instagram posts about the mpox outbreak. The work presented in this paper (stated above) aims to address this research gap. It presents this multilingual dataset of 60,127 Instagram posts about mpox, published between July 23, 2022, and September 5, 2024. This dataset contains Instagram posts about mpox in 52 languages.For each of these posts, the Post ID, Post Description, Date of publication, language, and translated version of the post (translation to English was performed using the Google Translate API) are presented as separate attributes in the dataset. After developing this dataset, sentiment analysis, hate speech detection, and anxiety or stress detection were also performed. This process included classifying each post intoone of the fine-grain sentiment classes, i.e., fear, surprise, joy, sadness, anger, disgust, or neutralhate or not hateanxiety/stress detected or no anxiety/stress detected.These results are presented as separate attributes in the dataset for the training and testing of machine learning algorithms for sentiment, hate speech, and anxiety or stress detection, as well as for other applications.The 52 distinct languages in which Instagram posts are present in the dataset are English, Portuguese, Indonesian, Spanish, Korean, French, Hindi, Finnish, Turkish, Italian, German, Tamil, Urdu, Thai, Arabic, Persian, Tagalog, Dutch, Catalan, Bengali, Marathi, Malayalam, Swahili, Afrikaans, Panjabi, Gujarati, Somali, Lithuanian, Norwegian, Estonian, Swedish, Telugu, Russian, Danish, Slovak, Japanese, Kannada, Polish, Vietnamese, Hebrew, Romanian, Nepali, Czech, Modern Greek, Albanian, Croatian, Slovenian, Bulgarian, Ukrainian, Welsh, Hungarian, and Latvian.The following is a description of the attributes present in this dataset:Post ID: Unique ID of each Instagram postPost Description: Complete description of each post in the language in which it was originally publishedDate: Date of publication in MM/DD/YYYY formatLanguage: Language of the post as detected using the Google Translate APITranslated Post Description: Translated version of the post description. All posts which were not in English were translated into English using the Google Translate API. No language translation was performed for English posts.Sentiment: Results of sentiment analysis (using the preprocessed version of the translated Post Description) where each post was classified into one of the sentiment classes: fear, surprise, joy, sadness, anger, disgust, and neutralHate: Results of hate speech detection (using the preprocessed version of the translated Post Description) where each post was classified as hate or not hateAnxiety or Stress: Results of anxiety or stress detection (using the preprocessed version of the translated Post Description) where each post was classified as stress/anxiety detected or no stress/anxiety detected.All the Instagram posts that were collected during this data mining process to develop this dataset were publicly available on Instagram and did not require a user to log in to Instagram to view the same (at the time of writing this paper).

  13. d

    Dataplex: Reddit Data | Global Social Media Data | 2.1M+ subreddits: trends,...

    • datarade.ai
    .json, .csv
    + more versions
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    Dataplex, Dataplex: Reddit Data | Global Social Media Data | 2.1M+ subreddits: trends, audience insights + more | Ideal for Interest-Based Segmentation [Dataset]. https://datarade.ai/data-products/dataplex-reddit-data-global-social-media-data-1-1m-mill-dataplex
    Explore at:
    .json, .csvAvailable download formats
    Dataset authored and provided by
    Dataplex
    Area covered
    Martinique, Gambia, Botswana, Macao, Holy See, Côte d'Ivoire, Christmas Island, Mexico, Jersey, Chile
    Description

    The Reddit Subreddit Dataset by Dataplex offers a comprehensive and detailed view of Reddit’s vast ecosystem, now enhanced with appended AI-generated columns that provide additional insights and categorization. This dataset includes data from over 2.1 million subreddits, making it an invaluable resource for a wide range of analytical applications, from social media analysis to market research.

    Dataset Overview:

    This dataset includes detailed information on subreddit activities, user interactions, post frequency, comment data, and more. The inclusion of AI-generated columns adds an extra layer of analysis, offering sentiment analysis, topic categorization, and predictive insights that help users better understand the dynamics of each subreddit.

    2.1 Million Subreddits with Enhanced AI Insights: The dataset covers over 2.1 million subreddits and now includes AI-enhanced columns that provide: - Sentiment Analysis: AI-driven sentiment scores for posts and comments, allowing users to gauge community mood and reactions. - Topic Categorization: Automated categorization of subreddit content into relevant topics, making it easier to filter and analyze specific types of discussions. - Predictive Insights: AI models that predict trends, content virality, and user engagement, helping users anticipate future developments within subreddits.

    Sourced Directly from Reddit:

    All social media data in this dataset is sourced directly from Reddit, ensuring accuracy and authenticity. The dataset is updated regularly, reflecting the latest trends and user interactions on the platform. This ensures that users have access to the most current and relevant data for their analyses.

    Key Features:

    • Subreddit Metrics: Detailed data on subreddit activity, including the number of posts, comments, votes, and user participation.
    • User Engagement: Insights into how users interact with content, including comment threads, upvotes/downvotes, and participation rates.
    • Trending Topics: Track emerging trends and viral content across the platform, helping you stay ahead of the curve in understanding social media dynamics.
    • AI-Enhanced Analysis: Utilize AI-generated columns for sentiment analysis, topic categorization, and predictive insights, providing a deeper understanding of the data.

    Use Cases:

    • Social Media Analysis: Researchers and analysts can use this dataset to study online behavior, track the spread of information, and understand how content resonates with different audiences.
    • Market Research: Marketers can leverage the dataset to identify target audiences, understand consumer preferences, and tailor campaigns to specific communities.
    • Content Strategy: Content creators and strategists can use insights from the dataset to craft content that aligns with trending topics and user interests, maximizing engagement.
    • Academic Research: Academics can explore the dynamics of online communities, studying everything from the spread of misinformation to the formation of online subcultures.

    Data Quality and Reliability:

    The Reddit Subreddit Dataset emphasizes data quality and reliability. Each record is carefully compiled from Reddit’s vast database, ensuring that the information is both accurate and up-to-date. The AI-generated columns further enhance the dataset's value, providing automated insights that help users quickly identify key trends and sentiments.

    Integration and Usability:

    The dataset is provided in a format that is compatible with most data analysis tools and platforms, making it easy to integrate into existing workflows. Users can quickly import, analyze, and utilize the data for various applications, from market research to academic studies.

    User-Friendly Structure and Metadata:

    The data is organized for easy navigation and analysis, with metadata files included to help users identify relevant subreddits and data points. The AI-enhanced columns are clearly labeled and structured, allowing users to efficiently incorporate these insights into their analyses.

    Ideal For:

    • Data Analysts: Conduct in-depth analyses of subreddit trends, user engagement, and content virality. The dataset’s extensive coverage and AI-enhanced insights make it an invaluable tool for data-driven research.
    • Marketers: Use the dataset to better understand your target audience, tailor campaigns to specific interests, and track the effectiveness of marketing efforts across Reddit.
    • Researchers: Explore the social dynamics of online communities, analyze the spread of ideas and information, and study the impact of digital media on public discourse, all while leveraging AI-generated insights.

    This dataset is an essential resource for anyone looking to understand the intricacies of Reddit's vast ecosystem, offering the data and AI-enhanced insights needed to drive informed decisions and strategies across various fields. Whether you’re tracking emerging trends, analyzing user behavior, or conduc...

  14. Z

    DeepCube: Post-processing and annotated datasets of social media data

    • data.niaid.nih.gov
    Updated Mar 15, 2024
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    Alexandros Mokas (2024). DeepCube: Post-processing and annotated datasets of social media data [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_7732930
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    Dataset updated
    Mar 15, 2024
    Dataset provided by
    Eleni Kamateri
    Giannis Tsampoulatidis
    Alexandros Mokas
    License

    Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
    License information was derived automatically

    Description

    Researcher(s): Alexandros Mokas, Eleni Kamateri

    Supervisor: Ioannis Tsampoulatidis

    This repository contains 3 social media datasets:

    2 Post-processing datasets: These datasets contain post-processing data extracted from the analysis of social media posts collected for two different use cases during the first two years of the Deepcube project. More specifically, these include:

    The UC2 dataset containing the post-processing analysis of the Twitter data collected for the DeepCube use case (UC2) dealing with the climate induced migration in Africa. This dataset contains in total 5,695,253 social media posts collected from the Twitter platform, based on the initial version of search criteria relevant to UC2 defined by Universitat De Valencia, focused on the regions of Ethiopia and Somalia and started from 26 June, 2021 till March, 2023.

    The UC5 dataset containing the post-processing analysis of the Twitter and Instagram data collected for the DeepCube use case (UC5) related to the sustainable and environmentally-friendly tourism. This dataset contains in total 58,143 social media posts collected from the Twitter and Instagram platform (12,881 collected from Twitter and 45,262 collected from Instagram), based on the initial version of search criteria relevant to UC5 defined by MURMURATION SAS, focused on the regions of Brasil and started from 26 June, 2021 till March, 2023.

    1 Annotated dataset: An additional anottated dataset was created that contains post-processing data along with annotations of Twitter posts collected for UC2 for the years 2010-2022. More specifically, it includes:

    The UC2 dataset contain the post-processing of the Twitter data collected for the DeepCube use case (UC2) dealing with the climate induced migration in Africa. This dataset contains in total 1721 annotated (412 relevant and 1309 irrelevant) by social media posts collected from the Twitter platform, focused on the region of Somalia and started from 1 January, 2010 till 31 December, 2022.

    For every social media post retrieved from Twitter and Instagram, a preprocessing step was performed. This involved a three-step analysis of each post using the appropriate web service. First, the location of the post was automatically extracted from the text using a location extraction service. Second, the images included in the post were analyzed using a concept extraction service, which identified and provided the top ten concepts that best described the image. These concepts included items such as "person," "building," "drought," "sun," and so on. Finally, the sentiment expressed in the post's text was determined by using a sentiment analysis service. The sentiment was classified as either positive, negative, or neutral.

    After the social media posts were preprocessed, they were visualized using the Social Media Web Application. This intuitive, user-friendly online application was designed for both expert and non-expert users and offers a web-based user interface for filtering and visualizing the collected social media data. The application provides various filtering options, an interactive map, a timeline, and a collection of graphs to help users analyze the data. Moreover, this application provides users with the option to download aggregated data for specific periods by applying filters and clicking the "Download Posts" button. This feature allows users to easily extract and analyze social media data outside of the web application, providing greater flexibility and control over data analysis.

    The dataset is provided by INFALIA. INFALIA, being a spin-off of the CERTH institute and a partner of a research EU project, releases this dataset containing Tweets IDs and post pre-processing data for the sole purpose of enabling the validation of the research conducted within the DeepCube. Moreover, Twitter Content provided in this dataset to third parties remains subject to the Twitter Policy, and those third parties must agree to the Twitter Terms of Service, Privacy Policy, Developer Agreement, and Developer Policy (https://developer.twitter.com/en/developer-terms) before receiving this download.

  15. Vietnamese Social Media Emotion Corpus

    • kaggle.com
    Updated Dec 29, 2022
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    Minh Thanh (2022). Vietnamese Social Media Emotion Corpus [Dataset]. https://www.kaggle.com/datasets/hmthanh/vsmec
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Dec 29, 2022
    Dataset provided by
    Kaggle
    Authors
    Minh Thanh
    Area covered
    Vietnam
    Description

    Emotion recognition is a higher approach or special case of sentiment analysis. In this task, the result is not produced in terms of either polarity: positive or negative or in the form of rating (from 1 to 5) but of a more detailed level of sentiment analysis in which the result are depicted in more expressions like sadness, enjoyment, anger, disgust, fear and surprise. Emotion recognition plays a critical role in measuring brand value of a product by recognizing specific emotions of customers’ comments. In this study, we have achieved two targets. First and foremost, we built a standard Vietnamese Social Media Emotion Corpus (UIT-VSMEC) with about 6,927 human-annotated sentences with six emotion labels, contributing to emotion recognition research in Vietnamese which is a low-resource language in Natural Language Processing (NLP). Secondly, we assessed and measured machine learning and deep neural network models on our UIT-VSMEC. As a result, Convolutional Neural Network (CNN) model achieved the highest performance with 57.61% of F1-score.

    Paper: Vong Ho, Duong Nguyen, Danh Nguyen, Linh Pham, Kiet Nguyen and Ngan Nguyen, Emotion Recognition for Vietnamese Social Media Text, 2019 16th International Conference of the Pacific Association for Computational Linguistics (PACLING 2019), October 11-13, 2019, Ha Noi, Vietnam. Link.

    https://sites.google.com/uit.edu.vn/uit-nlp/datasets-projects

  16. n

    fb-pages-company

    • networkrepository.com
    csv
    Updated Nov 15, 2017
    + more versions
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    Network Data Repository (2017). fb-pages-company [Dataset]. https://networkrepository.com/fb-pages-company.php
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    csvAvailable download formats
    Dataset updated
    Nov 15, 2017
    Dataset authored and provided by
    Network Data Repository
    License

    https://networkrepository.com/policy.phphttps://networkrepository.com/policy.php

    Description

    Mutually liked facebook pages. Nodes represent the pages and edges are mutual likes among them. - Data collected about Facebook pages (November 2017). These datasets represent blue verified Facebook page networks of different categories. Nodes represent the pages and edges are mutual likes among them.

  17. Z

    Data from: PANACEA dataset - Heterogeneous COVID-19 Claims

    • data.niaid.nih.gov
    • zenodo.org
    Updated Jul 15, 2022
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    Kochkina, Elena (2022). PANACEA dataset - Heterogeneous COVID-19 Claims [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_6493846
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    Dataset updated
    Jul 15, 2022
    Dataset provided by
    He, Yulan
    Kochkina, Elena
    Procter, Rob
    Arana-Catania, Miguel
    Zubiaga, Arkaitz
    Liakata, Maria
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The peer-reviewed publication for this dataset has been presented in the 2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), and can be accessed here: https://arxiv.org/abs/2205.02596. Please cite this when using the dataset.

    This dataset contains a heterogeneous set of True and False COVID claims and online sources of information for each claim.

    The claims have been obtained from online fact-checking sources, existing datasets and research challenges. It combines different data sources with different foci, thus enabling a comprehensive approach that combines different media (Twitter, Facebook, general websites, academia), information domains (health, scholar, media), information types (news, claims) and applications (information retrieval, veracity evaluation).

    The processing of the claims included an extensive de-duplication process eliminating repeated or very similar claims. The dataset is presented in a LARGE and a SMALL version, accounting for different degrees of similarity between the remaining claims (excluding respectively claims with a 90% and 99% probability of being similar, as obtained through the MonoT5 model). The similarity of claims was analysed using BM25 (Robertson et al., 1995; Crestani et al., 1998; Robertson and Zaragoza, 2009) with MonoT5 re-ranking (Nogueira et al., 2020), and BERTScore (Zhang et al., 2019).

    The processing of the content also involved removing claims making only a direct reference to existing content in other media (audio, video, photos); automatically obtained content not representing claims; and entries with claims or fact-checking sources in languages other than English.

    The claims were analysed to identify types of claims that may be of particular interest, either for inclusion or exclusion depending on the type of analysis. The following types were identified: (1) Multimodal; (2) Social media references; (3) Claims including questions; (4) Claims including numerical content; (5) Named entities, including: PERSON − People, including fictional; ORGANIZATION − Companies, agencies, institutions, etc.; GPE − Countries, cities, states; FACILITY − Buildings, highways, etc. These entities have been detected using a RoBERTa base English model (Liu et al., 2019) trained on the OntoNotes Release 5.0 dataset (Weischedel et al., 2013) using Spacy.

    The original labels for the claims have been reviewed and homogenised from the different criteria used by each original fact-checker into the final True and False labels.

    The data sources used are:

    The LARGE dataset contains 5,143 claims (1,810 False and 3,333 True), and the SMALL version 1,709 claims (477 False and 1,232 True).

    The entries in the dataset contain the following information:

    • Claim. Text of the claim.

    • Claim label. The labels are: False, and True.

    • Claim source. The sources include mostly fact-checking websites, health information websites, health clinics, public institutions sites, and peer-reviewed scientific journals.

    • Original information source. Information about which general information source was used to obtain the claim.

    • Claim type. The different types, previously explained, are: Multimodal, Social Media, Questions, Numerical, and Named Entities.

    Funding. This work was supported by the UK Engineering and Physical Sciences Research Council (grant no. EP/V048597/1, EP/T017112/1). ML and YH are supported by Turing AI Fellowships funded by the UK Research and Innovation (grant no. EP/V030302/1, EP/V020579/1).

    References

    • Arana-Catania M., Kochkina E., Zubiaga A., Liakata M., Procter R., He Y.. Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims. NAACL 2022 https://arxiv.org/abs/2205.02596

    • Stephen E Robertson, Steve Walker, Susan Jones, Micheline M Hancock-Beaulieu, Mike Gatford, et al. 1995. Okapi at trec-3. Nist Special Publication Sp,109:109.

    • Fabio Crestani, Mounia Lalmas, Cornelis J Van Rijsbergen, and Iain Campbell. 1998. “is this document relevant?. . . probably” a survey of probabilistic models in information retrieval. ACM Computing Surveys (CSUR), 30(4):528–552.

    • Stephen Robertson and Hugo Zaragoza. 2009. The probabilistic relevance framework: BM25 and beyond. Now Publishers Inc.

    • Rodrigo Nogueira, Zhiying Jiang, Ronak Pradeep, and Jimmy Lin. 2020. Document ranking with a pre-trained sequence-to-sequence model. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings, pages 708–718.

    • Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019. Bertscore: Evaluating text generation with bert. In International Conference on Learning Representations.

    • Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.

    • Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, et al. 2013. Ontonotes release 5.0 ldc2013t19. Linguistic Data Consortium, Philadelphia, PA, 23.

    • Limeng Cui and Dongwon Lee. 2020. Coaid: Covid-19 healthcare misinformation dataset. arXiv preprint arXiv:2006.00885.

    • Yichuan Li, Bohan Jiang, Kai Shu, and Huan Liu. 2020. Mm-covid: A multilingual and multimodal data repository for combating covid-19 disinformation.

    • Tamanna Hossain, Robert L. Logan IV, Arjuna Ugarte, Yoshitomo Matsubara, Sean Young, and Sameer Singh. 2020. COVIDLies: Detecting COVID-19 misinformation on social media. In Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, Online. Association for Computational Linguistics.

    • Ellen Voorhees, Tasmeer Alam, Steven Bedrick, Dina Demner-Fushman, William R Hersh, Kyle Lo, Kirk Roberts, Ian Soboroff, and Lucy Lu Wang. 2021. Trec-covid: constructing a pandemic information retrieval test collection. In ACM SIGIR Forum, volume 54, pages 1–12. ACM New York, NY, USA.

  18. Data_Sheet_1_Social Media Use and Mental Health and Well-Being Among...

    • frontiersin.figshare.com
    docx
    Updated May 30, 2023
    + more versions
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    Viktor Schønning; Gunnhild Johnsen Hjetland; Leif Edvard Aarø; Jens Christoffer Skogen (2023). Data_Sheet_1_Social Media Use and Mental Health and Well-Being Among Adolescents – A Scoping Review.docx [Dataset]. http://doi.org/10.3389/fpsyg.2020.01949.s001
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    docxAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    Frontiers Mediahttp://www.frontiersin.org/
    Authors
    Viktor Schønning; Gunnhild Johnsen Hjetland; Leif Edvard Aarø; Jens Christoffer Skogen
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Introduction: Social media has become an integrated part of daily life, with an estimated 3 billion social media users worldwide. Adolescents and young adults are the most active users of social media. Research on social media has grown rapidly, with the potential association of social media use and mental health and well-being becoming a polarized and much-studied subject. The current body of knowledge on this theme is complex and difficult-to-follow. The current paper presents a scoping review of the published literature in the research field of social media use and its association with mental health and well-being among adolescents.Methods and Analysis: First, relevant databases were searched for eligible studies with a vast range of relevant search terms for social media use and mental health and well-being over the past five years. Identified studies were screened thoroughly and included or excluded based on prior established criteria. Data from the included studies were extracted and summarized according to the previously published study protocol.Results: Among the 79 studies that met our inclusion criteria, the vast majority (94%) were quantitative, with a cross-sectional design (57%) being the most common study design. Several studies focused on different aspects of mental health, with depression (29%) being the most studied aspect. Almost half of the included studies focused on use of non-specified social network sites (43%). Of specified social media, Facebook (39%) was the most studied social network site. The most used approach to measuring social media use was frequency and duration (56%). Participants of both genders were included in most studies (92%) but seldom examined as an explanatory variable. 77% of the included studies had social media use as the independent variable.Conclusion: The findings from the current scoping review revealed that about 3/4 of the included studies focused on social media and some aspect of pathology. Focus on the potential association between social media use and positive outcomes seems to be rarer in the current literature. Amongst the included studies, few separated between different forms of (inter)actions on social media, which are likely to be differentially associated with mental health and well-being outcomes.

  19. P

    Sepehr_RumTel01 Dataset

    • paperswithcode.com
    • data.mendeley.com
    • +1more
    Updated Jan 15, 2022
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    Zoleikha Jahanbakhsh-Nagadeh; Mohammad-Reza Feizi-Derakhshi; Arash Sharifi (2020). Sepehr_RumTel01 Dataset [Dataset]. https://paperswithcode.com/dataset/sepehr-rumtel01
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    Dataset updated
    Jan 15, 2022
    Authors
    Zoleikha Jahanbakhsh-Nagadeh; Mohammad-Reza Feizi-Derakhshi; Arash Sharifi
    Description

    The expansion of social networks has accelerated the transmission of information and news at every communities. Over the past few years, the number of users, audiences and social networking publishers, are increased dramatically too. Among the massive amounts of information and news reported on these networks, we are faced with issues that have not been verified which is called “rumors”. Identifying rumors on social networks is carried out in the form of rumor detection approaches; the massive amount of these news and information force to use the machine learning techniques. The most important problem with auto-detection approaches is the lack of a database of rumors. For that matter, in this article, a collection of rumors published on the social network “telegrams” have been collected. These data are gathered from five Persian-language channels that have specially reviewed this issue. The collected data set contains 3283 messages with 2829 attachments, having a volume of over 1.6 gigabytes. This dataset can also be used for different purposes of natural language processing.

  20. Developer Community and Code Datasets

    • datarade.ai
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    Oxylabs, Developer Community and Code Datasets [Dataset]. https://datarade.ai/data-products/developer-community-and-code-datasets-oxylabs
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    .bin, .json, .xml, .csv, .xls, .sql, .txtAvailable download formats
    Dataset authored and provided by
    Oxylabs
    Area covered
    Bahamas, El Salvador, Philippines, Tuvalu, Marshall Islands, United Kingdom, South Sudan, Guyana, Saint Pierre and Miquelon, Djibouti
    Description

    Unlock the power of ready-to-use data sourced from developer communities and repositories with Developer Community and Code Datasets.

    Data Sources:

    1. GitHub: Access comprehensive data about GitHub repositories, developer profiles, contributions, issues, social interactions, and more.

    2. StackShare: Receive information about companies, their technology stacks, reviews, tools, services, trends, and more.

    3. DockerHub: Dive into data from container images, repositories, developer profiles, contributions, usage statistics, and more.

    Developer Community and Code Datasets are a treasure trove of public data points gathered from tech communities and code repositories across the web.

    With our datasets, you'll receive:

    • Usernames;
    • Companies;
    • Locations;
    • Job Titles;
    • Follower Counts;
    • Contact Details;
    • Employability Statuses;
    • And More.

    Choose from various output formats, storage options, and delivery frequencies:

    • Get datasets in CSV, JSON, or other preferred formats.
    • Opt for data delivery via SFTP or directly to your cloud storage, such as AWS S3.
    • Receive datasets either once or as per your agreed-upon schedule.

    Why choose our Datasets?

    1. Fresh and accurate data: Access complete, clean, and structured data from scraping professionals, ensuring the highest quality.

    2. Time and resource savings: Let us handle data extraction and processing cost-effectively, freeing your resources for strategic tasks.

    3. Customized solutions: Share your unique data needs, and we'll tailor our data harvesting approach to fit your requirements perfectly.

    4. Legal compliance: Partner with a trusted leader in ethical data collection. Oxylabs is trusted by Fortune 500 companies and adheres to GDPR and CCPA standards.

    Pricing Options:

    Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

    Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

    Experience a seamless journey with Oxylabs:

    • Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
    • Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
    • Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
    • Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

    Empower your data-driven decisions with Oxylabs Developer Community and Code Datasets!

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Stacy Jo Dixon (2025). Instagram accounts with the most followers worldwide 2024 [Dataset]. https://www.statista.com/topics/1164/social-networks/
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Instagram accounts with the most followers worldwide 2024

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Dataset updated
Jun 17, 2025
Dataset provided by
Statistahttp://statista.com/
Authors
Stacy Jo Dixon
Description

Cristiano Ronaldo has one of the most popular Instagram accounts as of April 2024.

              The Portuguese footballer is the most-followed person on the photo sharing app platform with 628 million followers. Instagram's own account was ranked first with roughly 672 million followers.

              How popular is Instagram?

              Instagram is a photo-sharing social networking service that enables users to take pictures and edit them with filters. The platform allows users to post and share their images online and directly with their friends and followers on the social network. The cross-platform app reached one billion monthly active users in mid-2018. In 2020, there were over 114 million Instagram users in the United States and experts project this figure to surpass 127 million users in 2023.

              Who uses Instagram?

              Instagram audiences are predominantly young – recent data states that almost 60 percent of U.S. Instagram users are aged 34 years or younger. Fall 2020 data reveals that Instagram is also one of the most popular social media for teens and one of the social networks with the biggest reach among teens in the United States.

              Celebrity influencers on Instagram
              Many celebrities and athletes are brand spokespeople and generate additional income with social media advertising and sponsored content. Unsurprisingly, Ronaldo ranked first again, as the average media value of one of his Instagram posts was 985,441 U.S. dollars.
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