A Instagram (beauty influencers) dataset of 436,843 posts from beauty influencers, collected by Seungbae Kim and Jinyoung Han (2020). Each record contains post caption text, like count, comment count, video/ad flags, tagged user count, location presence, image dimensions, timestamp, and influencer follower count. The dataset is notable for its subset of 9.7M-post Instagram influencer dataset covering the beauty category, with rich per-post engagement signals and influencer-level metadata. Intended for research into influencer marketing analysis, engagement prediction, social media content analysis, and sponsorship detection. This package holds the post metadata (one JSON .info file per post) and influencer lists; the post images, which the authors distribute separately on request, are not included.
A YouTube dataset of 39,147 videos, 762,840 related-video edges, collected by Cheng et al. via YouTube API (2007-2008). Each record contains video metadata (ID, uploader, age, category, length, views, rate, ratings, comments), related video IDs. Early YouTube snapshot 0502 capturing the video graph and metadata from the platform's rapid growth period in 2007-2008. Suitable for social network analysis, video recommendation, information diffusion, community detection.
A YouTube dataset of 62,978 videos, 774,403 related-video edges, collected by Cheng et al. via YouTube API (2007-2008). Each record contains video metadata (ID, uploader, age, category, length, views, rate, ratings, comments), related video IDs. Early YouTube snapshot 080707 capturing the video graph and metadata from the platform's rapid growth period in 2007-2008. Suitable for social network analysis, video recommendation, information diffusion, community detection.
A YouTube dataset of 61,977 videos, 755,227 related-video edges, collected by Cheng et al. via YouTube API (2007-2008). Each record contains video metadata (ID, uploader, age, category, length, views, rate, ratings, comments), related video IDs. Early YouTube snapshot 080701 capturing the video graph and metadata from the platform's rapid growth period in 2007-2008. Suitable for social network analysis, video recommendation, information diffusion, community detection.
The citation network of the arXiv High Energy Physics – Phenomenology (hep-ph) section, from SNAP (the KDD Cup 2003 data), as distributed by KONECT (cit-HepPh): 421,578 citations among 34,546 papers posted between January 1993 and April 2003. An edge points from a citing paper to the paper it cites. Useful for citation analysis, scientific impact and the growth of networks over time.
A YouTube dataset of 45,268 videos, 838,482 related-video edges, collected by Cheng et al. via YouTube API (2007-2008). Each record contains video metadata (ID, uploader, age, category, length, views, rate, ratings, comments), related video IDs. Early YouTube snapshot 0315 capturing the video graph and metadata from the platform's rapid growth period in 2007-2008. Suitable for social network analysis, video recommendation, information diffusion, community detection.
A YouTube dataset of 45,176 videos, 830,991 related-video edges, collected by Cheng et al. via YouTube API (2007-2008). Each record contains video metadata (ID, uploader, age, category, length, views, rate, ratings, comments), related video IDs. Early YouTube snapshot 0320 capturing the video graph and metadata from the platform's rapid growth period in 2007-2008. Suitable for social network analysis, video recommendation, information diffusion, community detection.
A YouTube dataset of 42,503 videos, 785,781 related-video edges, collected by Cheng et al. via YouTube API (2007-2008). Each record contains video metadata (ID, uploader, age, category, length, views, rate, ratings, comments), related video IDs. Early YouTube snapshot 0316 capturing the video graph and metadata from the platform's rapid growth period in 2007-2008. Suitable for social network analysis, video recommendation, information diffusion, community detection.
A YouTube dataset of 59,058 videos, 703,002 related-video edges, collected by Cheng et al. via YouTube API (2007-2008). Each record contains video metadata (ID, uploader, age, category, length, views, rate, ratings, comments), related video IDs. Early YouTube snapshot 080623 capturing the video graph and metadata from the platform's rapid growth period in 2007-2008. Suitable for social network analysis, video recommendation, information diffusion, community detection.
A YouTube dataset of 55,428 videos, 710,335 related-video edges, collected by Cheng et al. via YouTube API (2007-2008). Each record contains video metadata (ID, uploader, age, category, length, views, rate, ratings, comments), related video IDs. Early YouTube snapshot 080621 capturing the video graph and metadata from the platform's rapid growth period in 2007-2008. Suitable for social network analysis, video recommendation, information diffusion, community detection.
A YouTube dataset of 58,063 videos, 675,259 related-video edges, collected by Cheng et al. via YouTube API (2007-2008). Each record contains video metadata (ID, uploader, age, category, length, views, rate, ratings, comments), related video IDs. Early YouTube snapshot 080703 capturing the video graph and metadata from the platform's rapid growth period in 2007-2008. Suitable for social network analysis, video recommendation, information diffusion, community detection.
A YouTube dataset of 60,152 videos, 710,000 related-video edges, collected by Cheng et al. via YouTube API (2007-2008). Each record contains video metadata (ID, uploader, age, category, length, views, rate, ratings, comments), related video IDs. Early YouTube snapshot 080727 capturing the video graph and metadata from the platform's rapid growth period in 2007-2008. Suitable for social network analysis, video recommendation, information diffusion, community detection.
A YouTube dataset of 54,906 videos, 683,286 related-video edges, collected by Cheng et al. via YouTube API (2007-2008). Each record contains video metadata (ID, uploader, age, category, length, views, rate, ratings, comments), related video IDs. Early YouTube snapshot 080709 capturing the video graph and metadata from the platform's rapid growth period in 2007-2008. Suitable for social network analysis, video recommendation, information diffusion, community detection.
A YouTube dataset of 53,165 videos, 645,238 related-video edges, collected by Cheng et al. via YouTube API (2007-2008). Each record contains video metadata (ID, uploader, age, category, length, views, rate, ratings, comments), related video IDs. Early YouTube snapshot 080625 capturing the video graph and metadata from the platform's rapid growth period in 2007-2008. Suitable for social network analysis, video recommendation, information diffusion, community detection.
A YouTube dataset of 50,216 videos, 623,801 related-video edges, collected by Cheng et al. via YouTube API (2007-2008). Each record contains video metadata (ID, uploader, age, category, length, views, rate, ratings, comments), related video IDs. Early YouTube snapshot 080723 capturing the video graph and metadata from the platform's rapid growth period in 2007-2008. Suitable for social network analysis, video recommendation, information diffusion, community detection.
A YouTube dataset of 41,835 videos, 784,710 related-video edges, collected by Cheng et al. via YouTube API (2007-2008). Each record contains video metadata (ID, uploader, age, category, length, views, rate, ratings, comments), related video IDs. Early YouTube snapshot 0306 capturing the video graph and metadata from the platform's rapid growth period in 2007-2008. Suitable for social network analysis, video recommendation, information diffusion, community detection.
A YouTube dataset of 52,130 videos, 640,597 related-video edges, collected by Cheng et al. via YouTube API (2007-2008). Each record contains video metadata (ID, uploader, age, category, length, views, rate, ratings, comments), related video IDs. Early YouTube snapshot 080629 capturing the video graph and metadata from the platform's rapid growth period in 2007-2008. Suitable for social network analysis, video recommendation, information diffusion, community detection.
A YouTube dataset of 54,024 videos, 602,777 related-video edges, collected by Cheng et al. via YouTube API (2007-2008). Each record contains video metadata (ID, uploader, age, category, length, views, rate, ratings, comments), related video IDs. Early YouTube snapshot 080715 capturing the video graph and metadata from the platform's rapid growth period in 2007-2008. Suitable for social network analysis, video recommendation, information diffusion, community detection.
A Instagram (interior influencers) dataset of 348,559 posts from interior influencers, collected by Seungbae Kim and Jinyoung Han (2020). Each record contains post caption text, like count, comment count, video/ad flags, tagged user count, location presence, image dimensions, timestamp, and influencer follower count. The dataset is notable for its subset of 9.7M-post Instagram influencer dataset covering the interior category, with rich per-post engagement signals and influencer-level metadata. Intended for research into influencer marketing analysis, engagement prediction, social media content analysis, and sponsorship detection. This package holds the post metadata (one JSON .info file per post) and influencer lists; the post images, which the authors distribute separately on request, are not included.
A YouTube dataset of 51,513 videos, 622,451 related-video edges, collected by Cheng et al. via YouTube API (2007-2008). Each record contains video metadata (ID, uploader, age, category, length, views, rate, ratings, comments), related video IDs. Early YouTube snapshot 080705 capturing the video graph and metadata from the platform's rapid growth period in 2007-2008. Suitable for social network analysis, video recommendation, information diffusion, community detection.
A YouTube dataset of 48,705 videos, 584,997 related-video edges, collected by Cheng et al. via YouTube API (2007-2008). Each record contains video metadata (ID, uploader, age, category, length, views, rate, ratings, comments), related video IDs. Early YouTube snapshot 080713 capturing the video graph and metadata from the platform's rapid growth period in 2007-2008. Suitable for social network analysis, video recommendation, information diffusion, community detection.
A YouTube dataset of 46,826 videos, 565,222 related-video edges, collected by Cheng et al. via YouTube API (2007-2008). Each record contains video metadata (ID, uploader, age, category, length, views, rate, ratings, comments), related video IDs. Early YouTube snapshot 080721 capturing the video graph and metadata from the platform's rapid growth period in 2007-2008. Suitable for social network analysis, video recommendation, information diffusion, community detection.
A Amazon (co-purchasing network) dataset of 334,863 nodes and 925,872 edges, collected by Yang & Leskovec from Amazon website (2012). Each record contains product-product co-purchasing edges and product-category communities. The dataset is notable for its product co-purchasing network with ground-truth communities from categories. Intended for research into community detection, recommendation systems, and product relationship analysis.
A YouTube dataset of 48,842 videos, 570,756 related-video edges, collected by Cheng et al. via YouTube API (2007-2008). Each record contains video metadata (ID, uploader, age, category, length, views, rate, ratings, comments), related video IDs. Early YouTube snapshot 080717 capturing the video graph and metadata from the platform's rapid growth period in 2007-2008. Suitable for social network analysis, video recommendation, information diffusion, community detection.
A Instagram (fitness influencers) dataset of 319,414 posts from fitness influencers, collected by Seungbae Kim and Jinyoung Han (2020). Each record contains post caption text, like count, comment count, video/ad flags, tagged user count, location presence, image dimensions, timestamp, and influencer follower count. The dataset is notable for its subset of 9.7M-post Instagram influencer dataset covering the fitness category, with rich per-post engagement signals and influencer-level metadata. Intended for research into influencer marketing analysis, engagement prediction, social media content analysis, and sponsorship detection. This package holds the post metadata (one JSON .info file per post) and influencer lists; the post images, which the authors distribute separately on request, are not included.
A DBLP (co-authorship network) dataset of 317,080 nodes and 1,049,866 edges, collected by Yang & Leskovec from DBLP bibliography (2012). Each record contains author-author co-authorship edges and publication-venue communities. The dataset is notable for its academic collaboration network with ground-truth communities from publication venues. Intended for research into community detection, network evolution, and scientific collaboration analysis.
A Meme Descriptions (Cheezburger dataset) dataset of 252,284 meme descriptions and 117 images, collected by Wang and Wen via Cheezburger (2015). Each record contains meme image and text descriptions, popularity scores. The dataset is notable for its multimodal dataset for meme popularity prediction and generation. Intended for research into multimodal learning, meme generation, popularity prediction, and computational humor.
A StackExchange SuperUser (technical Q&A community) dataset of 194,085 users and 1,443,339 directed interaction edges, collected by Paranjape, Benson & Leskovec via SNAP (2017). Each record contains source user ID, target user ID, Unix timestamp of interaction. Temporal directed interaction network from SuperUser covering answers, comments on answers, and comments on questions — three types of user interactions collapsed into a single edge list with Unix timestamps.. Suitable for temporal network analysis, Q&A community structure, motif detection, information flow in technical communities.
A Gnutella peer-to-peer network dataset combining the 9 SNAP snapshots of August 2002 (Ripeanu et al.): 191,327 nodes and 506,442 directed edges in all. SNAP numbers the hosts of each snapshot separately, so the snapshots sit side by side in one graph with node IDs of the form snapshot:host, and no host is linked across snapshots. Suitable for temporal network analysis, graph evolution, and peer-to-peer network resilience.
A TED dataset of 0 posts, 190,414 responses, collected by Peixian and Zhang. Each record contains posts, responses, op_gender, responder_gender, sentiment, relevance. rtGender dataset for TED — studies gendered responses in online discourse. Suitable for gender studies, sentiment analysis, response prediction.
A Instagram (pet influencers) dataset of 169,998 posts from pet influencers, collected by Seungbae Kim and Jinyoung Han (2020). Each record contains post caption text, like count, comment count, video/ad flags, tagged user count, location presence, image dimensions, timestamp, and influencer follower count. The dataset is notable for its subset of 9.7M-post Instagram influencer dataset covering the pet category, with rich per-post engagement signals and influencer-level metadata. Intended for research into influencer marketing analysis, engagement prediction, social media content analysis, and sponsorship detection. This package holds the post metadata (one JSON .info file per post) and influencer lists; the post images, which the authors distribute separately on request, are not included.
A Douban (Chinese social network) dataset of 154,908 users and 327,162 friendship edges, collected by Long Qiu via Douban crawl (December 2010). Each record contains user-user friendship edges and user IDs. The dataset is notable for its friendship network from Douban, China's largest online community for book, movie, and music reviews and recommendations. Intended for research into social network analysis, community detection, Chinese social media research, and content-sharing platform structure studies.
A X (Twitter) with Community Notes annotations dataset of 152,266 multimodal posts (21,845 AI-generated, 104,915 miscaptioned), collected by Chrysidis, Papadopoulos & Papadopoulos via X Community Notes (2026). Each record contains tweet ID, reply/favorite/view/retweet counts, Community Notes text and timestamp, and misinformation type label (miscaptioned / ai_generated / other). Large-scale dataset of 152,266 multimodal X posts with Community Notes — tracking the rise of AI-generated misinformation with 21,845 confirmed AI-generated posts. Includes engagement metrics showing that AI-generated content achieves disproportionate virality despite slower initial reporting.. Suitable for AI-generated content detection, misinformation classification, Community Notes effectiveness, multimodal misinformation analysis, virality and engagement modeling, temporal degradation of detectors.
Synthetic data: 150,000 generated social-media posts about Nigerian telecom operators (MTN, Airtel, Glo, 9mobile), published on Hugging Face by THUgewu as 'africa-synth'. The posts are not real user content. The file groups them into 978 rows by platform, operator, city and sentiment, with one list per field (post ID, time, text, issue category, likes, shares, comments, reach, sentiment score, operator response). Suitable for testing sentiment and customer-service pipelines, not for studying real users.
A ReCOVery (news credibility repository) dataset of 2,029 news articles with reliability labels and social media engagement, collected by Zhou, Mulay, Ferrara, and Zafarani via news websites and Twitter (2020). Each record contains news article title and body, reliability label (1=reliable, 0=unreliable), publisher, political bias, country, author, and social media engagement data. The dataset is notable for its multimodal repository combining news content with social media engagement, designed for COVID-19 news credibility research. Intended for research into fake news detection, credibility assessment, political bias analysis, and multimodal misinformation research.
A StackExchange Ask Ubuntu (Linux/Ubuntu Q&A community) dataset of 137,517 users and 280,102 directed interaction edges, collected by Paranjape, Benson & Leskovec via SNAP (2017). Each record contains source user ID, target user ID, Unix timestamp of interaction. Temporal directed network of answer-to-question interactions on Ask Ubuntu — edges connect answering users to question-asking users with Unix timestamps, capturing the flow of technical knowledge.. Suitable for Q&A network analysis, temporal interaction patterns, community detection, knowledge flow in technical communities.
A Reddit (r/funny, r/pics, r/gifs and other popular subreddits) dataset of 132,214 Reddit posts across 837 subreddits with full comment threads, collected by Web scraper collecting top Reddit posts circa 2012. Each record contains post ID, subreddit, post title, upvotes, downvotes, submission timestamp, and full comment text for each thread. Complete HTML snapshots of 132,214 Reddit threads from ~2012, preserving the full discussion including post content, comment trees, and engagement metrics across 837 subreddits.. Suitable for community detection, Reddit discussion analysis, social engagement modeling, comment thread structure, subreddit community behavior.
A Twitter dataset of 128,484 tweets with political leaning labels, collected by Chatelet via Hugging Face. Each record contains text, political label (liberal, conservative, neutral). 100K tweets labeled for political ideology with balanced classes. Suitable for political NLP, ideology classification, discourse analysis.
A LiveJournal social network of 106,577 users and 961,392 directed edges (793,948 follower links and 246,164 friendship links), from a crawl of the users within three hops of the seed user krazykitkat, pre-processed for the paper 'Emotions in Social Networks: Distributions, Patterns, and Models' (Jin and Zafarani): only users with 10 or more posts carrying a predefined LiveJournal mood were kept, and user ids are anonymized. This copy holds the two network files (Friendship.txt, Followers.txt) and the release README; the per-post mood file (Posts.txt) and community memberships (Community.txt) that the README describes are not included. Suitable for social network structure, follower and friendship analysis, community detection, social influence modeling.
A Livemocha (language learning community) dataset of 104,103 users and 2,193,083 friendship edges, collected by Xia Hu (Ben) via Livemocha crawl, December 2010. Each record contains user-user friendship edges. The dataset is notable for its friendship network of the world's largest online language learning community, with 6M+ members across 200+ countries. Intended for research into social network analysis, community detection, and cross-cultural interaction studies.
A BlogCatalog (v2) dataset of 97,884 bloggers and 1,668,698 friendship edges, collected by Agarwal et al. / Tang & Liu via BlogCatalog crawl. Each record contains blogger-blogger friendship edges, user IDs. The dataset is notable for its blogosphere friendship network from BlogCatalog, used for familiar stranger detection and latent social dimensions research. Intended for research into social network analysis, community detection, group membership prediction, and blogosphere structure studies.
A YouTube dataset of 10,296 videos, 198,320 related-video edges, collected by Cheng et al. via YouTube API (2007-2008). Each record contains video metadata (ID, uploader, age, category, length, views, rate, ratings, comments), related video IDs. Early YouTube snapshot 0302 capturing the video graph and metadata from the platform's rapid growth period in 2007-2008. Suitable for social network analysis, video recommendation, information diffusion, community detection.
A BlogCatalog (blogging social network) dataset of 88,784 bloggers and 2,093,195 friendship edges, collected by Agarwal et al. via BlogCatalog crawl (July 2009). Each record contains blogger-blogger friendship edges and user IDs. The dataset is notable for its classic blogosphere friendship network used for familiar stranger detection and social identity research. Intended for research into social network analysis, community detection, familiar stranger identification, and blogosphere structure studies.
A Google Scholar dataset of 82,937 papers, 148,116 citations, collected by Google Scholar via academic citation data. Each record contains citation edges between papers, node IDs, node names (titles), node types (paper, author, etc.), edge types (cites, etc.). Academic citation network from Google Scholar, capturing the flow of scientific knowledge through citation relationships. Suitable for citation analysis, academic network analysis, research impact, knowledge flow studies, scholarly communication.
A Twitter (ego-networks) dataset of 81,306 nodes and 1,768,149 edges, collected by McAuley & Leskovec via Twitter public crawl (2012). Each record contains directed user-user follow edges and ego-network circles. The dataset is notable for its directed ego-network dataset with ground-truth social circles. Intended for research into social network analysis, influence propagation, and community detection.
A Flickr (photo-sharing) dataset of 80,513 users and 5,899,882 friendship edges, collected by Tang & Liu via Flickr crawl (2009). Each record contains user-user friendship edges, group memberships (195 groups), and user IDs. The dataset is notable for its classic photo-sharing social network with explicit group memberships, used as a benchmark for latent social dimensions. Intended for research into social network analysis, community detection, group membership prediction, and photo-sharing platform structure studies.
A Gnutella (August 31, 2002) dataset of 62,586 nodes and 147,892 edges, collected by Ripeanu et al. via Gnutella crawl (2002). Each record contains directed peer-to-peer connections in the Gnutella network. The dataset is notable for its snapshot from August 31, 2002, part of a temporal sequence of 9 snapshots. Intended for research into peer-to-peer network analysis, temporal graph evolution, and network resilience studies.
A Brightkite (location-based social network) dataset of 58,228 users, 214,078 friendship edges, and 4.49M check-ins, collected by Cho et al. via Brightkite API (2008-2010). Each record contains undirected friendship edges, check-in records (timestamp, latitude, longitude, location_id). The dataset is notable for its location-based social network with rich spatiotemporal check-in data for mobility research. Intended for research into human mobility analysis, location-based social networks, friendship prediction, and urban computing.
A ISOT (fake news dataset) dataset of 44,898 news articles labeled fake/real, collected by ISOT via Reuters and various sources (2017). Each record contains news article text (title + content) and binary label (is_fake: 1=fake, 0=real). The dataset is notable for its well-balanced dataset of fake and real news articles for misinformation research. Intended for research into fake news detection, text classification, and misinformation analysis.
A Twitter dataset of 38,918 users and 32,523 directed, weighted reply edges (36,902 replies in all), collected by De Domenico et al. via the Twitter Streaming API around the Higgs boson announcement, 1-7 July 2012. Each record holds the replying user ID, the replied-to user ID and the number of replies between them. Complements the Higgs retweet and mention networks from the same collection. Suitable for information diffusion, scientific communication on social media and reply network structure.
Instagram
YouTube
Amazon
Stack Exchange
Douban
Twitter (X)
Facebook
Reddit
LiveJournal
Livemocha
BlogCatalog
Academia.edu
Google
Flickr
Gnutella
Brightkite