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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 PubMed dataset of 19,717 nodes, 44,327 edges, 3 classes, collected by Yang et al. (ICML 2016). Each record contains node features, citation edges, class labels. PubMed network with bag-of-words features. Suitable for node classification, semi-supervised learning, graph neural networks.
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 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 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 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 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 Bitcoin OTC (peer-to-peer Bitcoin trading platform) dataset of 5,881 users and 35,592 directed weighted trust ratings (32,029 positive, 3,563 negative), collected by Kumar et al. via platform crawl, distributed by SNAP. Each record contains directed weighted signed edges between users: integer rating in [-10, +10] representing trust strength, plus binarized sign (+1/-1) for compatibility with signed network algorithms; Unix timestamps per rating. continuous integer trust weights rather than binary +1/-1 signs, enabling weighted signed network analysis and providing finer-grained trust signal than Epinions or Slashdot while operating in the high-stakes context of peer-to-peer Bitcoin trading. Suitable for weighted signed network analysis, edge weight prediction, trust propagation modeling, fraud detection, reputation system design, and signed graph embedding research.
A Bitcoin Alpha (peer-to-peer Bitcoin trading platform) dataset of 3,783 users and 24,186 directed weighted trust ratings (22,650 positive, 1,536 negative), collected by Kumar et al. via platform crawl, distributed by SNAP. Each record contains directed weighted signed edges between users: integer rating in [-10, +10] representing trust strength, plus binarized sign (+1/-1) for compatibility with signed network algorithms; Unix timestamps per rating. continuous integer trust weights rather than binary +1/-1 signs, enabling weighted signed network analysis and providing finer-grained trust signal than Epinions or Slashdot while operating in the high-stakes context of peer-to-peer Bitcoin trading. Suitable for weighted signed network analysis, edge weight prediction, trust propagation modeling, fraud detection, reputation system design, and signed graph embedding research.
A CiteSeer dataset of 3,327 nodes, 4,676 edges, 6 classes, collected by Yang et al. (ICML 2016). Each record contains node features, citation edges, class labels. CiteSeer network with bag-of-words features. Suitable for node classification, semi-supervised learning, graph neural networks.
A Cora dataset of 2,708 nodes, 5,278 edges, 7 classes, collected by Yang et al. (ICML 2016). Each record contains node features, citation edges, class labels. Cora network with bag-of-words features. Suitable for node classification, semi-supervised learning, graph neural networks.
A Terrorist Relations dataset of 881 nodes, 8,592 edges, 4 classes, collected by Yang et al. (ICML 2016). Each record contains node features, citation edges, class labels. Terrorist Relations network with bag-of-words features. Suitable for node classification, semi-supervised learning, graph neural networks.
A Terrorist Attacks dataset of 645 nodes, 3,172 edges, 6 classes, collected by Yang et al. (ICML 2016). Each record contains node features, citation edges, class labels. Terrorist Attacks network with bag-of-words features. Suitable for node classification, semi-supervised learning, graph neural networks.
A WebKB wisconsin dataset of 265 web pages, 479 hyperlinks, collected by Yang et al. (ICML 2016). Each record contains web page features, hyperlink edges, class labels. WebKB wisconsin university web page classification dataset. Suitable for node classification, web page categorization, graph neural networks.
A WebKB washington dataset of 230 web pages, 417 hyperlinks, collected by Yang et al. (ICML 2016). Each record contains web page features, hyperlink edges, class labels. WebKB washington university web page classification dataset. Suitable for node classification, web page categorization, graph neural networks.
A WebKB cornell dataset of 195 web pages, 286 hyperlinks, collected by Yang et al. (ICML 2016). Each record contains web page features, hyperlink edges, class labels. WebKB cornell university web page classification dataset. Suitable for node classification, web page categorization, graph neural networks.
A WebKB texas dataset of 187 web pages, 298 hyperlinks, collected by Yang et al. (ICML 2016). Each record contains web page features, hyperlink edges, class labels. WebKB texas university web page classification dataset. Suitable for node classification, web page categorization, graph neural networks.
Wikipedia hoax and non-hoax articles from Kumar, West & Leskovec, 'Disinformation on the Web' (WWW 2016), distributed by SNAP. The README describes 64 hoaxes, each paired with a non-hoax article created the same day with similar appearance; the parsed folders hold those 64 + 64 articles, while the cleaned-markup folder holds 76 hoaxes and 64 non-hoaxes (140 articles). The archive is SNAP's own and also contains 65 macOS '._' resource-fork files, which are not articles. Released under the CRAPL licence (CRAPL-LICENSE.txt is included).
Twitter (X)
Amazon
WebKB
Wikipedia