TY - GEN
T1 - Detecting social bots by jointly modeling deep behavior and content information
AU - Cai, Chiyu
AU - Li, Linjing
AU - Zeng, Daniel
N1 - Funding Information:
This work was supported in part by the National Key R&D Program of China under Grant No. 2016QY02D0205, the National Natural Science Foundation of China under Grant Nos. 71202169, 71602184, 71621002, 61671450, U1435221, the Key Research Program of the Chinese Academy of Sciences under Grant No. ZDRW-XH-2017-3, as well as the Early Career Development Award of SKLMCCS.
Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/11/6
Y1 - 2017/11/6
N2 - 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.
AB - 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.
KW - Behavior factors
KW - Bot detection
KW - Deep learning
KW - Temporal content
UR - http://www.scopus.com/inward/record.url?scp=85037371397&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85037371397&partnerID=8YFLogxK
U2 - 10.1145/3132847.3133050
DO - 10.1145/3132847.3133050
M3 - Conference contribution
AN - SCOPUS:85037371397
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 1995
EP - 1998
BT - CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management
PB - Association for Computing Machinery
T2 - 26th ACM International Conference on Information and Knowledge Management, CIKM 2017
Y2 - 6 November 2017 through 10 November 2017
ER -