A Twitter dataset of 263,555 tweets with binary event labels (14,129 disruptive events, 248,499 non-events) related to Bitcoin and cryptocurrency markets, collected by Mehta et al. via Twitter API, 2021. Each record contains tweet text, binary event/non-event label, follower count, location, username, Bitcoin price at time of tweet, and retweet count. one of few datasets explicitly linking social media content to financial market disruption events, with synchronized Bitcoin price data enabling direct study of social signal → market movement relationships. Suitable for event detection, financial market prediction from social media, cryptocurrency sentiment analysis, and disruptive event classification.
A NDS-VU signed network repository dataset of 5,882 nodes and 21,492 signed edges (synthetic Bitcoin OTC variant), collected by NDS-VU (Vrije Universiteit Amsterdam). Each record contains directed signed edges between nodes: sign (+1 or -1) representing positive/negative relationships; no edge weights beyond binary sign. Synthetic version of the Bitcoin OTC web-of-trust network with reversed edge polarities, enabling adversarial and robustness testing of signed network algorithms. Suitable for signed network analysis, algorithm benchmarking, robustness testing.
A Bitcoin OTC (peer-to-peer Bitcoin trading platform) dataset of 5,881 users and 35,592 directed weighted trust ratings (32,029 positive, 3,563 negative), collected by Kumar et al. via platform crawl, distributed by SNAP. Each record contains directed weighted signed edges between users: integer rating in [-10, +10] representing trust strength, plus binarized sign (+1/-1) for compatibility with signed network algorithms; Unix timestamps per rating. continuous integer trust weights rather than binary +1/-1 signs, enabling weighted signed network analysis and providing finer-grained trust signal than Epinions or Slashdot while operating in the high-stakes context of peer-to-peer Bitcoin trading. Suitable for weighted signed network analysis, edge weight prediction, trust propagation modeling, fraud detection, reputation system design, and signed graph embedding research.
A Bitcoin Alpha (peer-to-peer Bitcoin trading platform) dataset of 3,783 users and 24,186 directed weighted trust ratings (22,650 positive, 1,536 negative), collected by Kumar et al. via platform crawl, distributed by SNAP. Each record contains directed weighted signed edges between users: integer rating in [-10, +10] representing trust strength, plus binarized sign (+1/-1) for compatibility with signed network algorithms; Unix timestamps per rating. continuous integer trust weights rather than binary +1/-1 signs, enabling weighted signed network analysis and providing finer-grained trust signal than Epinions or Slashdot while operating in the high-stakes context of peer-to-peer Bitcoin trading. Suitable for weighted signed network analysis, edge weight prediction, trust propagation modeling, fraud detection, reputation system design, and signed graph embedding research.
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