A LiveJournal (blogging social network) dataset of 12,404,868 emotion-labeled posts from 115,444 users with 106,577 nodes and 961,392 friendship/follower edges, collected by Jin & Zafarani via LiveJournal API, distributed by Data Lab SU. Each record contains user ID, emotion label (-1=negative/0=neutral/1=positive), timestamp, friendship edges, follower edges, community membership. 12,404,868 LiveJournal posts labeled with trichotomous emotion scores across 115,444 users — uniquely combines social network structure (friendship + follower graphs) with per-post emotion labels and community membership for studying emotion propagation in networks. Suitable for emotion propagation in social networks, community emotion analysis, sentiment diffusion, social influence on emotion, network-based mood modeling.
A Twitter dataset of 5,072,124 tweets from 1,835 congressional accounts, collected by Alex Litel via Twitter API (2017-2023). Each record contains tweet text, screen name, user ID, timestamp, retweet count, favorite count, source, retweet status. A daily collection of tweets from U.S. Congress members, committees, caucuses and party accounts, one JSON file per day; 11 days have no file in the source repository (for example 2022-01-15, 2022-03-17 and 2022-10-24 to 2022-10-31). metadata/historical-users-filtered.json, from the collection's companion repository, maps each account to its member or office, chamber, party and type. Suitable for political communication, sentiment analysis, election studies, congressional behavior, social media analysis.
A Slashdot dataset of 10,019 discussion threads, August 2005 to August 2006, from the Online Conversation Threads repository of Aragón et al. (ICWSM 2017): 2,084,864 nodes (articles and comments) and 2,075,611 comment-to-parent reply edges, with comment text, moderation scores, timestamps and thread depth. Suitable for online discourse analysis, conversation dynamics and hierarchical reply-tree structure.
A twitter dataset of 2,041,399 tweet IDs from all members of the 115th U.S. Congress, collected by Littman via Social Feed Manager (GWU Libraries). Each record contains tweet IDs only — full content requires hydration. Complete tweet archive from all 115th Congress members enabling legislator communication and retweet network analysis. Suitable for political discourse, legislator communication patterns, congressional social media behavior.
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 Barrapunto (the Spanish Slashdot) dataset of 7,724 discussion threads, 2005-2007, from the Online Conversation Threads repository of Aragón et al. (ICWSM 2017): 172,132 nodes (articles and comments) and 169,825 comment-to-parent reply edges, with comment text, moderation scores, timestamps and thread depth. Suitable for online discourse analysis, conversation dynamics and hierarchical reply-tree structure.
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 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 Math Overflow (ALL) dataset of 24,818 users and 239,978 temporal edges, collected by Paranjape, Benson, Leskovec via Stack Exchange Data Dump (2017). Each record contains directed temporal edges (all) between users with Unix timestamps. The dataset is notable for its ALL interaction type from a 6.4-year temporal network of academic Q&A. Intended for research into temporal network analysis, motif discovery, social interaction dynamics, and academic community analysis.
A Twitter (crowdsourced hate speech annotation) dataset of 24,783 tweets labeled as hate speech (1,430), offensive (19,190), or neither (4,163), collected by Davidson et al. via CrowdFlower crowdsourcing (2017). Each record contains tweet text, majority class label (0=hate/1=offensive/2=neither), per-label coder counts, total coder count. Davidson et al. 2017 is one of the most cited hate speech datasets, with 24,783 tweets annotated by multiple crowdworkers — distinguished by separating hate speech from merely offensive language. Suitable for hate speech detection, offensive language classification, content moderation, NLP benchmarking, crowdsourced annotation research.
A Math Overflow (A2Q) dataset of 21,688 users and 90,489 temporal edges, collected by Paranjape, Benson, Leskovec via Stack Exchange Data Dump (2017). Each record contains directed temporal edges (a2q) between users with Unix timestamps. The dataset is notable for its A2Q interaction type from a 6.4-year temporal network of academic Q&A. Intended for research into temporal network analysis, motif discovery, social interaction dynamics, and academic community analysis.
A Math Overflow (C2Q) dataset of 16,836 users and 101,329 temporal edges, collected by Paranjape, Benson, Leskovec via Stack Exchange Data Dump (2017). Each record contains directed temporal edges (c2q) between users with Unix timestamps. The dataset is notable for its C2Q interaction type from a 6.4-year temporal network of academic Q&A. Intended for research into temporal network analysis, motif discovery, social interaction dynamics, and academic community analysis.
A Math Overflow (C2A) dataset of 13,840 users and 81,121 temporal edges, collected by Paranjape, Benson, Leskovec via Stack Exchange Data Dump (2017). Each record contains directed temporal edges (c2a) between users with Unix timestamps. The dataset is notable for its C2A interaction type from a 6.4-year temporal network of academic Q&A. Intended for research into temporal network analysis, motif discovery, social interaction dynamics, and academic community analysis.
A LIAR (fact-checking benchmark) dataset of 12,789 labeled claim statements from Politifact, collected by Wang via Politifact fact-checking (2017). Each record contains claim text, 6-class veracity label, speaker metadata (party, state, job title), credit history counts (barely_true/false/half_true/mostly_true/pants_fire), and context. The dataset is notable for its benchmark dataset for fake news detection with fine-grained 6-class labels, rich speaker metadata, and credit history. Intended for research into fake news detection, fact-checking, political discourse analysis, and misinformation research.
A Twitter dataset of 4,465 labeled Twitter accounts (991 bots, 3474 genuine), collected by Ashu273k — curated subset of Cresci et al. (2017). Each record contains user profile features (followers, friends, statuses, favourites, listed count), binary bot/genuine label, user bio text. Small, clean subset of the Cresci-2017 dataset curated for rapid prototyping and benchmarking of bot detection models, with pre-extracted user-level features and binary labels. Suitable for bot detection, social spambot classification, feature selection for account authenticity, rapid prototyping of detection algorithms.
A European research institution internal email system dataset of 986 employees and 332,334 directed email interactions across 42 departments, collected by Leskovec et al. via SNAP (anonymized institutional email logs). Each record contains sender employee ID, recipient employee ID, Unix timestamp, and department label per employee (42 departments). Temporal directed email communication network from a European research institution with ground-truth department labels for all 986 employees — enabling evaluation of community detection algorithms against known organizational structure.. Suitable for community detection benchmarking, organizational network analysis, temporal email network analysis, higher-order graph clustering.
LiveJournal
Twitter (X)
Slashdot
Stack Exchange
Barrapunto