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• W2963806301 abstract "Within OSNs, many of our supposedly online friends may instead be fake accounts called social bots, part of large groups that purposely re-share targeted content. Here, we study retweeting behaviors on Twitter, with the ultimate goal of detecting retweeting social bots.We collect a dataset of 10M retweets. We design a novel visualization that we leverage to highlight benign and malicious patterns of retweeting activity. In this way, we uncover a ?normal retweeting pattern that is peculiar of human-operated accounts, and suspicious patterns related to bot activities. Then, we propose a bot detection technique that stems from the previous exploration of retweeting behaviors. Our technique, called Retweet-Buster (RTbust), leverages unsupervised feature extraction and clustering. An LSTM autoencoder converts the retweet time series into compact and informative latent feature vectors, which are then clustered with a hierarchical density-based algorithm. Accounts belonging to large clusters characterized by malicious retweeting patterns are labeled as bots. RTbust obtains excellent detection results, with F1=0.87, whereas competitors achieve F1?0.76.Finally, we apply RTbust to a large dataset of retweets, uncovering 2 previously unknown active botnets with hundreds of accounts." @default.
• W2963806301 created "2019-07-30" @default.
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• W2963806301 date "2019-06-26" @default.
• W2963806301 modified "2023-10-01" @default.
• W2963806301 title "RTbust" @default.
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• W2963806301 doi "https://doi.org/10.1145/3292522.3326015" @default.
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