Name
Abstract | ||
|---|---|---|
Bots are regarded as the most common kind of malwares in the era of Web 2.0. In recent years, Internet has been populated by hundreds of millions of bots, especially on social media. Thus, the demand on effective and efficient bot detection algorithms is more urgent than ever. Existing works have partly satisfied this requirement by way of laborious feature engineering. In this paper, we propose a deep bot detection model aiming to learn an effective representation of social user and then detect social bots by jointly modeling social behavior and content information. The proposed model learns the representation of social behavior by encoding both endogenous and exogenous factors which affect user behavior. As to the representation of content, we regard the user content as temporal text data instead of just plain text as be treated in other existing works to extract semantic information and latent temporal patterns. To the best of our knowledge, this is the first trial that applies deep learning in modeling social users and accomplishing social bot detection. Experiments on real world dataset collected from Twitter demonstrate the effectiveness of the proposed model.
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Year | Venue | Field |
|---|---|---|
2017 | CIKM | World Wide Web,Social media,Information retrieval,Computer science,Semantic information,Feature engineering,Plain text,Artificial intelligence,Deep learning,Machine learning,Encoding (memory),The Internet |
DocType | ISBN | Citations |
Conference | 978-1-4503-4918-5 | 1 |
PageRank | References | Authors |
0.34 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chiyu Cai | 1 | 1 | 0.68 |
| Linjing Li | 2 | 39 | 12.91 |
| Daniel Zeng | 3 | 2539 | 286.59 |