A LiveJournal (blogging social network) dataset of 12,404,868 emotion-labeled posts from 115,444 users with 106,577 nodes and 961,392 friendship/follower edges, collected by Jin & Zafarani via LiveJournal API, distributed by Data Lab SU. Each record contains user ID, emotion label (-1=negative/0=neutral/1=positive), timestamp, friendship edges, follower edges, community membership. 12,404,868 LiveJournal posts labeled with trichotomous emotion scores across 115,444 users — uniquely combines social network structure (friendship + follower graphs) with per-post emotion labels and community membership for studying emotion propagation in networks. Suitable for emotion propagation in social networks, community emotion analysis, sentiment diffusion, social influence on emotion, network-based mood modeling.
A Reddit dataset of 6,252,711 posts and comments, 6,279 AITA submissions, 6,239,711 nodes, 6,133,386 reply edges, collected by Zenodo contributor via Reddit API, September–October 2023. Each record contains post/comment id, reply graph (parent_id), title, VADER sentiment, readability (Fog + FK-GL), 10 emotion lexicon scores (anticipation, disgust, fear, joy, sadness, surprise, trust, anger, positive, negative), valence/arousal/dominance. 2M+ r/AmITheAsshole posts and comments with pre-computed emotion lexicon scores, readability metrics, and VADER sentiment across multiple time windows (all-time, monthly, weekly, yearly); includes full reply graph structure.. Suitable for Emotion dynamics in online moral judgment, reply network analysis, readability and engagement research, temporal thread growth modeling..
A TikTok dataset of 209,543 videos from 1,872 creators, collected by Lingbo et al.. Each record contains video_id, author_id, create_time, description, transcript, GPT summary, topic, emotion scores, music metadata. Large-scale TikTok dataset with video metadata, GPT-generated summaries, emotion scores, and 30-day engagement trajectories. Suitable for engagement analysis, creator behavior, viral prediction, social media dynamics.
A LiveJournal social network of 106,577 users and 961,392 directed edges (793,948 follower links and 246,164 friendship links), from a crawl of the users within three hops of the seed user krazykitkat, pre-processed for the paper 'Emotions in Social Networks: Distributions, Patterns, and Models' (Jin and Zafarani): only users with 10 or more posts carrying a predefined LiveJournal mood were kept, and user ids are anonymized. This copy holds the two network files (Friendship.txt, Followers.txt) and the release README; the per-post mood file (Posts.txt) and community memberships (Community.txt) that the README describes are not included. Suitable for social network structure, follower and friendship analysis, community detection, social influence modeling.
A MySpace dataset of 80,749 messages between 36,459 users (41,533 directed user pairs) over two months, collected by Šuvakov et al. (Dryad, 2013). Each record holds the sender, the recipient, the time in minutes since the start of the window, and the message's SentiStrength positive (1 to 5) and negative (-1 to -5) scores. Suitable for sentiment dynamics, dialogue patterns and temporal network analysis.
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.
LiveJournal
Reddit
TikTok
Myspace
Menéame