Every dataset indexed by ECHO, A to Z.
1,179 datasets.
A twitter dataset of 767,139 tweet IDs from #BlackLivesMatter and #AllLivesMatter (10% Gardenhose sample, Aug 2014–Aug 2015), collected by Gallagher (Northeastern University, 2018). Each record contains tweet IDs only — full content requires hydration. Paired collection of #BLM and #ALM tweets enabling contrastive social movement analysis. Suitable for social movement analysis, racial discourse, political polarization.
A twitter dataset of 957,956 tweet IDs from the #MeToo movement (Q3 2020), collected by Maiorana, Morales Henry, and Weintraub via Social Feed Manager. Each record contains tweet IDs only — full content requires hydration. Quarterly snapshot of #MeToo discourse enabling longitudinal analysis when combined with other quarterly collections. Suitable for social movement analysis, gender studies, harassment detection, discourse trends.
A Twitter (#NoMore hashtag campaign) dataset of 1,048,574 tweets from the Ethiopian #NoMore movement (2021-11 to 2022-08), collected by Zenodo contributors via Twitter API. Each record contains tweet text, language, timestamp; Amharic+English bilingual subset also available. 1,048,574 tweets covering the #NoMore solidarity movement in Ethiopia and Eritrea — one of few African social movement datasets with Amharic content, spanning 2021-11 to 2022-08. Suitable for African social movement analysis, multilingual NLP, Amharic text processing, political communication research, hashtag activism, Global South social media studies.
A #petpeeve (annoying behaviors) dataset of 3,375 tweets labeled with annoying behavior categories, collected by Wang and Yang via Twitter API (2015). Each record contains tweet text and annoying behavior category label. The dataset is notable for its dataset for automatic categorization of annoying behaviors using lexical and frame-semantic embeddings. Intended for research into social media analysis, text classification, and behavioral categorization.
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 Bluesky Social dataset of approximately 1.8 million public posts, collected by Alpin Dale from the AT Protocol firehose API in 2024. Each record contains post text, author handle, timestamp, image presence flag, and reply-to URI for constructing directed reply-thread networks. multilingual corpus from a decentralized AT Protocol platform with English dominant alongside Spanish, Portuguese, Japanese, and other languages. Suitable for decentralized social media analysis, reply network structure, and multilingual content modeling.
A twitter dataset of ~280 million tweet IDs from the 2016 U.S. presidential election period, collected by Littman, Wrubel, and Kerchner via Social Feed Manager (GWU Libraries). Each record contains tweet IDs only — full content requires hydration. One of the largest election tweet archives, covering the full 2016 campaign including debates, conventions, and election night. Suitable for election discourse, political polarization, misinformation spread, retweet diffusion.
A twitter dataset of 295,670,111 tweet IDs from the 2020 U.S. presidential election, collected by Wrubel and Kerchner via Social Feed Manager (GWU Libraries). Each record contains tweet IDs only — full content requires hydration. Complete tweet archive from the 2020 election including post-election period and January 6 lead-up. Suitable for election discourse, misinformation, political polarization, post-election unrest.
A Bluesky (2024 presidential debate coverage) dataset of 279,249 bluesky posts from June 25 - July 2, 2024, collected by Zenodo contributors via public API. Each record contains post text, user ID, post ID, creation date, hashtags, mentioned users, URLs, post type (post/reply/repost), linked post, root post, language. 279,249 bluesky posts covering the 2024 Biden-Trump presidential debate with pre-computed sentence embeddings. Suitable for political communication, debate analysis, cross-platform comparison, decentralized social media research, NLP with embeddings.
A Mastodon (2024 presidential debate coverage) dataset of 73,789 mastodon posts from June 25 - July 2, 2024, collected by Zenodo contributors via public API. Each record contains post text, user ID, post ID, creation date, hashtags, mentioned users, URLs, post type (post/reply/repost), linked post, root post, language. 73,789 mastodon posts covering the 2024 Biden-Trump presidential debate with pre-computed sentence embeddings. Suitable for political communication, debate analysis, cross-platform comparison, decentralized social media research, NLP with embeddings.
A 4chan (/pol/) dataset of 4,998,945 posts from /pol/, collected by kjj0 via 4chan API. Each record contains post text content. A 5 million post sample from 4chan's /pol/ board, capturing political discourse, extremism, and online communities. Suitable for misinformation, propaganda, hate speech, political communication, online communities, extremist discourse.
Posts from 329 thematically distinct 'general' thread collections on 4chan's /pol/ board, November 2013 to May 2019, released by Emilija Jokubauskaitė and Stijn Peeters (Zenodo 3603292). 13,756,579 posts in 58,687 threads, one CSV per general plus an index file. Each row holds the thread ID, post ID, timestamp, body text, subject, poster name, image file name and MD5, and the poster's country code and name. Suitable for studying recurring political discussion threads, propaganda, hate speech and online subcultures.
A 4chan (/pol/) dataset of 499,723 posts with moderation scores, collected by kjj0 via 4chan API and OpenAI moderation model. Each record contains post text, sexual score, hate score, violence score, self-harm score, sexual/minors score, hate/threatening score, violence/graphic score. A dataset of 500,000 /pol/ posts with OpenAI moderation scores, enabling research on content moderation, hate speech detection, and harmful content analysis. Suitable for content moderation, hate speech detection, toxicity analysis, misinformation, political communication.
A Academia.edu dataset of 200,169 academics, 1,022,883 connections, collected by Fire et al. (2011) via Academia.edu platform. Each record contains undirected edges between academics, representing follows or connections between researchers on the platform. Academic social network capturing researcher connections on Academia.edu, a platform for sharing research papers and tracking scholarly impact. Suitable for link prediction, academic network analysis, scholarly collaboration studies, research impact analysis.
A David Copperfield novel (word adjacency network) dataset of 112 nodes and 425 edges, collected by M.E.J. Newman. Each record contains directed: False, weighted: False, signed: False. Word adjacency network of adjectives and nouns in David Copperfield — classic linguistic network benchmark.. Suitable for linguistic network analysis, community detection benchmark.
A Advogato dataset of 6,551 users, 51,317 trust relationships, collected by Massa et al. (2009) via Advogato platform. Each record contains directed, weighted trust edges between users, representing explicit trust relationships expressed by users. Advogato trust network where users express weighted trust relationships, enabling research on trust propagation and social influence in online communities. Suitable for trust network analysis, influence propagation, social influence studies, trust-based recommendation systems.
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 Amazon.com movie product pages dataset of 7,911,696 movie reviews spanning 1997 to 2012, collected by McAuley & Leskovec via Amazon product review pages (2013). Each record contains product ID, reviewer user ID, reviewer profile name, helpfulness votes (numerator/denominator), star rating (1-5), Unix timestamp, review summary, and full review text. One of the largest Amazon review datasets publicly available — 8 million movie reviews with full text, helpfulness votes, and star ratings spanning 15 years of Amazon movie reviews.. Suitable for sentiment analysis, review helpfulness prediction, recommendation systems, user expertise modeling, longitudinal review behavior analysis.
A Amazon.com (product reviews and ratings) dataset of 3,376,972 nodes and 5,743,258 edges, collected by J. McAuley & J. Leskovec via Amazon product pages. Each record contains directed: False, weighted: True, temporal: True. Large bipartite user-product rating network with timestamps and 1-5 star ratings — 5.8M edges across 2.1M users and 1.2M products.. Suitable for recommender systems, rating prediction, bipartite network analysis, e-commerce research.
A Amazon dataset of 1,396,896 QA pairs across 21 categories, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Comprehensive Amazon product Q&A data across 21 categories. Suitable for question answering, sentiment analysis, product understanding, conversational AI. The record count covers the single-answer files (qa_*.json.gz, one question and answer per line). The package also holds the multiple-answer release used in Wan and McAuley's ICDM 2016 paper (icdm/QA_*.json.gz, 17 categories: 172,617 products, 1,025,628 questions and 4,019,744 answers, with asker and answerer IDs and helpfulness votes), and McAuley's data page (index.html). Both are written as Python literals, not strict JSON (read them with ast.literal_eval).
A Amazon dataset of 9,011 QA pairs in Appliances, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Appliances. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 21,262 QA pairs in Arts Crafts & Sewing, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Arts Crafts & Sewing. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 89,923 QA pairs in Automotive, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Automotive. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 28,933 QA pairs in Baby, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Baby. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 42,422 QA pairs in Beauty, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Beauty. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 85,865 QA pairs in Cell Phones & Accessories, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Cell Phones & Accessories. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 22,068 QA pairs in Clothing Shoes & Jewelry, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Clothing Shoes & Jewelry. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 314,263 QA pairs in Electronics, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Electronics. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 19,538 QA pairs in Grocery & Gourmet Food, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Grocery & Gourmet Food. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 80,496 QA pairs in Health & Personal Care, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Health & Personal Care. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 184,439 QA pairs in Home & Kitchen, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Home & Kitchen. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 12,136 QA pairs in Industrial & Scientific, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Industrial & Scientific. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 23,322 QA pairs in Musical Instruments, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Musical Instruments. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 43,608 QA pairs in Office Products, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Office Products. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 59,595 QA pairs in Patio Lawn & Garden, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Patio Lawn & Garden. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 36,607 QA pairs in Pet Supplies, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Pet Supplies. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 10,636 QA pairs in Software, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Software. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 146,891 QA pairs in Sports & Outdoors, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Sports & Outdoors. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 101,088 QA pairs in Tools & Home Improvement, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Tools & Home Improvement. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 51,486 QA pairs in Toys & Games, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Toys & Games. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Amazon dataset of 13,307 QA pairs in Video Games, collected by Wan and McAuley via Amazon API. Each record contains question, answer, question type, answer type, ASIN, timestamp. Amazon product Q&A data for Video Games. Suitable for question answering, sentiment analysis, product understanding, conversational AI.
A Twitter dataset of 346,573 nodes, 1,091,552 edges, collected by Shao et al. via Hoaxy API (OSoMe/Indiana University). Each record contains from_user_id, to_user_id, tweet_id, tweet_created_at, site_type (claim/fact-checking). Retweet diffusion network of misinformation and fact-checking content surrounding the 2016 US election; distinguishes claim vs. fact-checking retweets.. Suitable for Misinformation diffusion, fact-checking network analysis, network epidemiology..
A Anybeat dataset of 12,645 users, 49,132 connections, collected by Fire et al. (2012) via Anybeat platform. Each record contains undirected edges between Anybeat users representing social connections in the community platform. Anybeat social network capturing user connections in a public gathering place where people interact with others from around their neighborhood or across the world. Suitable for link prediction, social network analysis, community detection, online community behavior studies.
A University Rovira i Virgili (email communication network) dataset of 1,133 nodes and 5,451 edges, collected by R. Guimerà et al.. Each record contains directed: False, weighted: False, signed: False. Email communication network of a university — classic benchmark for community detection algorithms.. Suitable for community detection, email network analysis, graph algorithm benchmarking.
A Jazz musician collaboration (recordings 1912–1940) dataset of 198 nodes and 2,742 edges, collected by P. Gleiser & L. Danon. Each record contains directed: False, weighted: False, signed: False. Collaboration network of jazz musicians who recorded together — classic small-world benchmark.. Suitable for community detection, small-world networks, graph algorithm benchmarking.
A C. elegans metabolic network dataset of 453 nodes and 2,040 edges, collected by H. Jeong et al.. Each record contains directed: False, weighted: True, signed: False. Metabolic network of C. elegans — classic biological network benchmark.. Suitable for biological network analysis, community detection, scale-free network research.
A PGP (Pretty Good Privacy) key signing network dataset of 10,680 nodes and 24,316 edges, collected by M. Boguñá et al.. Each record contains directed: False, weighted: False, signed: False. Web of trust network from PGP key signings — nodes are PGP users, edges are key signatures.. Suitable for trust network analysis, community detection, graph algorithm benchmarking.
A arXiv Astrophysics (collaboration network 1992–2003) dataset of 18,771 nodes and 198,050 edges, collected by J. Leskovec et al. via arXiv. Each record contains directed: False, weighted: False. Co-authorship network of astrophysics papers on arXiv — nodes are authors, edges connect co-authors.. Suitable for collaboration network analysis, community detection, academic social networks.
The co-authorship network of the arXiv High Energy Physics – Phenomenology (hep-ph) section, as distributed by KONECT (ca-cit-HepPh), from SNAP's arXiv data: 28,093 authors linked by 4,596,803 collaboration records, one for each pair of authors on a paper, published between March 1992 and March 2002. The records form 3,148,447 distinct co-author pairs, each timestamped with its paper's date. Useful for studying scientific collaboration, the growth and densification of networks over time, and community structure.
A arXiv High Energy Physics — Theory (1991–2003) dataset of 27,770 nodes and 352,807 edges, collected by J. Leskovec et al. via arXiv. Each record contains directed: True, weighted: False. Citation network of HEP-TH papers on arXiv — directed, covers theoretical physics literature.. Suitable for citation network analysis, academic influence, graph mining.
Twitter (X)
Bluesky
Mastodon
4chan
Academia.edu
Advogato
Amazon