A MADOC (Reddit, pics) dataset of 3,453,264 users and 15,863,415 reply edges from 51,914,975 interactions, collected by Dankulov et al. via platform APIs, published at ICWSM 2025. Each record contains user-user reply edges, post and comment text, per-record sentiment scores (VADER and TextBlob), subjectivity scores, and toxicity scores (ToxiGen). FAIR-compliant dataset with pre-computed NLP quality labels (sentiment, subjectivity, toxicity) per record, enabling network analysis and content quality research without additional annotation. Suitable for social network analysis, toxicity detection, cross-platform community dynamics, misinformation research, and hate speech propagation studies.
A MADOC (Reddit, gifs) dataset of 1,894,135 users and 6,785,726 reply edges from 21,090,022 interactions, collected by Dankulov et al. via platform APIs, published at ICWSM 2025. Each record contains user-user reply edges, post and comment text, per-record sentiment scores (VADER and TextBlob), subjectivity scores, and toxicity scores (ToxiGen). The dataset is notable for its FAIR-compliant dataset with pre-computed NLP quality labels (sentiment, subjectivity, toxicity) per record, enabling network analysis and content quality research without additional annotation. Intended for research into social network analysis, toxicity detection, cross-platform community dynamics, misinformation research, and hate speech propagation studies.
A Fakeddit (multimodal fake news benchmark) dataset of 965,647 Reddit submissions with text, images and metadata, collected by Nakamura et al. via the Reddit API (2020). Each record holds the post title, text, image URL, comment count, score, timestamp and 2-, 3- and 6-way veracity labels (6-way: true, satire/parody, misleading content, imposter content, false connection, manipulated content). Suitable for fake news detection, multimodal learning and misinformation classification.
A Reddit (cross-subreddit post collection) dataset of 31,725 posts across 91 subreddits, collected by EL-Amrany via Reddit API. Each record contains post title, subreddit, upvotes, score, upvote ratio, comment count, comment sentiment, title sentiment, image URL/format/dimensions, virality label (binary), and temporal features (hour, day, weekday, month, year). Reddit-V contains 31,725 Reddit posts labeled for virality (12.7% viral), with pre-computed engagement signals and sentiment scores designed for virality prediction benchmarking. Suitable for virality prediction, engagement modeling, multimodal analysis, sentiment analysis, content popularity research, social media classification benchmarks.
