A Reddit dataset of 35,776 subreddits and 286,561 directed hyperlinks (265,491 positive sentiment, 21,070 negative sentiment) from post hyperlinks embedded in post bodies, collected by Kumar et al. via Reddit API, January 2014 to April 2017, distributed by SNAP. Each record contains directed edges from source to target subreddit with binary sentiment label (+1/-1) and 86-dimension text property vector; post ID and timestamp per edge. captures cross-community interaction and conflict on Reddit with sentiment polarity per hyperlink, enabling study of how communities reference each other positively vs. antagonistically over time. Suitable for community conflict detection, cross-community interaction modeling, signed network analysis, temporal network analysis, and online community dynamics research.
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.
A YikYak / Reddit dataset of 26,304 posts with engagement and controversy labels, collected by Riana Doctor via YikYak API and Reddit scraping (2025). Each record contains post text, vote total, comment count, controversy flag, timestamp, user alias, group ID. Dataset combining YikYak-style anonymous posts and Reddit content with explicit engagement and controversy annotations, enabling comparative analysis of controversial content across platforms. Suitable for controversy detection, engagement prediction, content moderation, sentiment analysis, social media dynamics.
23,992 YouTube comments with sentiment and tone labels and short pros/cons notes, published on Hugging Face by AnandforU, in train (19,992), validation (2,000) and test (2,000) splits. Each record holds the comment text, sentiment, tone, pros and cons. Suitable for sentiment analysis and comment moderation.
A MADOC (Voat, gifs) dataset of 5,022 users and 11,435 reply edges from 21,573 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 MADOC (Voat, milliondollarextreme) dataset of 3,063 users and 10,819 reply edges from 21,190 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.
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 Meneame dataset of 14,995 articles, 20,000 comments, 45,392 users, collected by Meneame Media Bias Team. Each record contains article title, text, bias labels, sentiment, emotion scores, karma features, user-outlet graph. Spanish social news aggregator dataset with media bias labels and rich interaction features. Suitable for media bias detection, sentiment analysis, social news dynamics, graph analysis.
A Twitter dataset of 11,929 labeled financial tweets, collected by zeroshot via Twitter API. Each record contains tweet text, sentiment label (positive/negative/neutral), sentiment value (0/1/2), train/valid split. A labeled dataset of tweets about financial news, with sentiment annotations for stock market-related content. Suitable for sentiment analysis, financial NLP, stock market prediction, social media analytics, natural language processing.
A MADOC (Voat, KotakuInAction) dataset of 1,878 users and 3,854 reply edges from 7,853 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 Twitter dataset of 5,171 sentiment-labeled tweets about NFL teams, collected by Cameron C (Kaggle user cammyc) via Twitter API. Each record contains tweet text, sentiment (positive/negative/neutral), team, username, timestamp, confidence score. A curated dataset of 3,701 NFL-related tweets with sentiment labels, enabling research on sports fan sentiment, team-specific public opinion, and social media engagement patterns during the NFL season. Suitable for sentiment analysis, sports marketing, fan engagement, public opinion tracking, temporal sentiment analysis.
A Reddit dataset of 5,024 annotated comments, 809,713 total comments, collected by Behavioral Data Science Lab. Each record contains comment text, stance labels, interaction graphs, polarity scores. r/Brexit subreddit with crowd-annotated stance labels and temporal interaction graphs. Suitable for political polarization, opinion dynamics, stance detection.
A MADOC (Voat, MensRights) dataset of 992 users and 1,628 reply edges from 3,735 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 #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 MADOC (Voat, CringeAnarchy) dataset of 876 users and 1,740 reply edges from 3,030 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 Reddit dataset of 365 posts about Bundesliga football teams, collected by Reddit Data Archive via Reddit API. Each record contains post content, votes (score), comment count, team label, date, author. Small dataset of Reddit posts about Bundesliga football teams, capturing fan discussions and engagement across multiple clubs.. Suitable for sports analytics, sentiment analysis, fan community dynamics, engagement prediction.
A Twitter (aggregate) dataset of 4,709,404 topic-related tweets across 6 social issues, collected by NORC at the University of Chicago. Each record contains date, topic, daily volume, VADER compound sentiment, pos/neg ratio, account type (community/bot/news/government/activist), US state. Aggregate Twitter data on six politically salient social issues with daily volume and VADER sentiment scores 2016-2022, including unique bot-vs-community account type breakdowns and US state-level marijuana discourse data. Suitable for political communication, sentiment trends, bot detection, state-level public opinion, longitudinal social issue analysis.
Reddit
Yik Yak
YouTube
Voat
Yelp
Menéame
Twitter (X)