Gif

3 datasets tagged as the tag “Gif”.

3 datasets.

MADOC: Reddit / gifs Analyzed

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.

21.1M records1.9M nodes6.8M edges2025

Voat — v/gifs Analyzed

A Voat (Reddit alternative, 2013–2020) dataset of 6,624 submissions, 56,845 comments, 13,362 users, 35,153 collaboration edges, collected by Mekacher & Papasavva (ICWSM 2022) via full platform crawl. Each record contains user nodes (registration date, moderation status), directed weighted edges (commenter→submitter, weight=upvotes+1), submission titles and bodies, comment text, upvotes, downvotes, depth. Complete dataset for the Voat subverse v/gifs, covering its full lifetime on the platform (2013–2020). Includes all 6,624 submissions and 56,845 comments with full text and engagement metadata.. Suitable for social network analysis, community dynamics, content moderation, platform migration studies, online community research.

54.4K records13.4K nodes35.2K edges2022

MADOC: Voat / gifs Analyzed

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.

21.6K records5K nodes11.4K edges2025