A Delicious social bookmarking platform (user-item network) dataset of 34,611,302 nodes and 101,798,957 edges, collected by KONECT via Delicious API. Each record contains directed: False, weighted: True, temporal: True. Massive temporal bipartite network of Delicious users and bookmarked URLs — 301M edges across 35M users and items with Unix timestamps.. Suitable for social bookmarking analysis, recommendation systems, large-scale bipartite networks.
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).
The BookCrossing user–book network in its implicit form, as distributed by KONECT (bookcrossing_full-rating): 1,149,739 interactions between 105,278 members of the BookCrossing community and 340,523 books, collected by Cai-Nicolas Ziegler in August and September 2004. An edge records that a user interacted with a book; this version carries no rating values. Useful for collaborative filtering, implicit-feedback recommendation and the structure of user–item networks.
A Stack Exchange (magento.stackexchange.com) dataset of 31,898 users, 112,494 answer-collaboration edges, 113,069 questions, 144,532 answers, collected by Stack Exchange, Inc. via the annual Stack Exchange Data Dump. Each record contains user nodes (reputation, badge counts, vote history), directed weighted edges (answerer→asker, weight=answer score, count=interactions, accepted=bool), post text, tag distribution. User collaboration network from magento.stackexchange.com — directed weighted graph where edges represent knowledge-sharing relationships (A answered B's question), with edge weight reflecting answer quality. Enables prestige hierarchy, community detection, and expertise flow analysis.. Suitable for expertise network analysis, reputation systems, community detection, knowledge flow, PageRank-based authority ranking, Q&A platform dynamics..
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 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 Discogs music marketplace and database dataset of 270,786 nodes and 481,661 edges, collected by KONECT via Discogs database. Each record contains directed: False, weighted: True, temporal: False. Bipartite network linking Discogs users to music genres they collect — 4.1M edges across 270k users and 15 genres.. Suitable for recommender systems, music preference analysis, bipartite network analysis.
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.
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 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 Epinions.com (consumer review platform) dataset of 131,828 users and 841,372 directed signed edges (717,667 trust, 123,705 distrust), collected by Richardson et al. via Epinions.com crawl, distributed by SNAP. Each record contains directed signed edges between users: +1 indicates explicit trust, -1 indicates explicit distrust, capturing both positive and negative social relationships. one of the largest publicly available signed social networks, with explicit user-declared trust and distrust labels rather than inferred sentiment — making it ideal for signed graph algorithm benchmarking. Suitable for signed network analysis, trust propagation, link sign prediction, community detection in signed graphs, and balance theory validation.
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 Jester Online Joke Recommender System (UC Berkeley) dataset of 73,521 nodes and 4,136,360 edges, collected by K. Goldberg et al. via Jester system. Each record contains directed: False, weighted: True, temporal: False. Bipartite user-joke rating network with continuous ratings from -10 to +10 — one of the earliest large-scale collaborative filtering datasets.. Suitable for recommender systems, collaborative filtering, continuous rating prediction.
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 Reddit (r/gunsforsale ban intervention, 2018) dataset of 5,837 pre-ban posts from banned community, 33,758 pre-ban control posts, 5,222 post-ban control posts, collected by Cerulli et al. via Reddit API (ICWSM 2026). Each record contains comment ID, selftext (post body), author (pseudonymized), subreddit, score, created_utc, split label (in-before/out-before/out-after). TBBT r/gunsforsale: pre- and post-ban Reddit dataset from The Big Ban Theory — standardized 3-month window around the 2018 moderation intervention with matched control communities. Suitable for content moderation effects, community migration, deplatforming research, pre/post intervention analysis, Reddit ban studies.
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 SkyTrax dataset of 41,396 airline reviews from 362 airlines, collected by quankiquanki via SkyTrax (2014-2020). Each record contains review text, airline name, traveller type, cabin flown, overall rating, seat comfort, cabin staff, food and beverages, inflight entertainment, ground service, WiFi, value for money, and recommendation status. A comprehensive collection of airline reviews with detailed sub-ratings across multiple service dimensions, enabling granular sentiment analysis and service quality research. Suitable for sentiment analysis, service quality prediction, recommendation systems, customer satisfaction research.
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 Stack Exchange (hardwarerecs.stackexchange.com) dataset of 2,641 users, 3,472 answer-collaboration edges, 4,354 questions, 4,064 answers, collected by Stack Exchange, Inc. via the annual Stack Exchange Data Dump. Each record contains user nodes (reputation, badge counts, vote history), directed weighted edges (answerer→asker, weight=answer score, count=interactions, accepted=bool), post text, tag distribution. User collaboration network from hardwarerecs.stackexchange.com — directed weighted graph where edges represent knowledge-sharing relationships (A answered B's question), with edge weight reflecting answer quality. Enables prestige hierarchy, community detection, and expertise flow analysis.. Suitable for expertise network analysis, reputation systems, community detection, knowledge flow, PageRank-based authority ranking, Q&A platform dynamics..
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 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 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.
Yelp coffee-shop reviews for computational branding analytics (Wang, Lin & Kominek, EMNLP 2013): 17,894 reviews in train (10,495), dev (3,617) and test (3,782) splits. Each row holds the brand (Starbucks, Dunkin', other), satisfaction (negative, positive, neutral), the reviewer's gender (missing as '?' in some rows), US region, and the review text. There are no review, business or date fields. Research use only, per the README.
A Escort review website (sex worker review community) dataset of 16,730 nodes and 39,044 edges, collected by Rocha et al. via web crawl. Each record contains directed: False, weighted: True, signed: True, temporal: True. Bipartite signed network of escort reviews: clients rate escorts +1 (positive), 0 (neutral), or -1 (negative), with Unix timestamps. One of the few large-scale signed bipartite networks available.. Suitable for signed network analysis, bipartite network analysis, trust propagation.
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.
Delicious
Amazon
BookCrossing
Stack Exchange
Discogs
Douban
Facebook
Instagram
Twitter (X)
Epinions
Reddit
Skytrax
Yelp