Abstract
Due to the popularity of Twitter, it attracts malicious users’ interests. Most of previous approaches relied on account-based features such as message similarity between tweets, following-followers ratio, and so on. Account-based features can be easily manipulated by spam accounts. Spam collusion is a new way to escape the detection mechanisms. Therefore, we need an advanced mechanism to identify the spam collusion relations.
We exploit spam campaign which spreads spam tweets. We focus on the tweet with the high retweet count. We create the message-passing graph via the retweet relations, following relations, and retweet time, then we extract the time evolution feature in the aspect of graph structure. The latent behavior indexing technique is used to extract critical concepts for spam collusion recognition. We collect 5 million tweets from May 14, 2014 to July 15, 2014 and the ground-truth has been labeled by domain experts. Our approach can achieve 86% accuracy.
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Chen, PC., Lee, HM., Tyan, HR., Wu, JS., Wei, TE. (2014). Detecting Spam on Twitter via Message-Passing Based on Retweet-Relation. In: Cheng, SM., Day, MY. (eds) Technologies and Applications of Artificial Intelligence. TAAI 2014. Lecture Notes in Computer Science(), vol 8916. Springer, Cham. https://doi.org/10.1007/978-3-319-13987-6_6
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DOI: https://doi.org/10.1007/978-3-319-13987-6_6
Publisher Name: Springer, Cham
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