Abstract
Social spammers disseminate unsolicited information on social media sites that negatively impacts social networking systems. To detect social spammers, traditional methods leverage social network structures to identify the behavioral patterns hidden in their social interactions. They focus on accounts that are affiliated with groups comprising known spammers. However, since different parties are emerging to generate various spammers, they may form different kinds of groups, and some spammers may even detach from the flock. Therefore, it is challenging for existing methods to find the optimal group structure that captures different spammers simultaneously. Employing different approaches for specific spammers is time-consuming, and it also lacks the adaptivity of dealing with emerging spammers. In this work, we aim to propose a group modeling framework that adaptively characterizes social interactions of spammers. In particular, we introduce to integrate content information into the group modeling process. The proposed framework exploits additional content information in selecting groups and individuals that are likely to be involved in spamming activities. In order to alleviate the intensive computational cost, we transform the problem as a sparse learning task that can be solved efficiently. Experimental results on realworld datasets show that the proposed method outperforms the state-of-the-art approaches.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the 11th International Conference on Web and Social Media, ICWSM 2017 |
| Publisher | AAAI Press |
| Pages | 319-326 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781577357889 |
| State | Published - 2017 |
| Event | 11th International Conference on Web and Social Media, ICWSM 2017 - Montreal, Canada Duration: May 15 2017 → May 18 2017 |
Other
| Other | 11th International Conference on Web and Social Media, ICWSM 2017 |
|---|---|
| Country | Canada |
| City | Montreal |
| Period | 5/15/17 → 5/18/17 |
Fingerprint
ASJC Scopus subject areas
- Computer Networks and Communications
Cite this
Adaptive spammer detection with sparse group modeling. / Wu, Liang; Hu, Xia; Morstatter, Fred; Liu, Huan.
Proceedings of the 11th International Conference on Web and Social Media, ICWSM 2017. AAAI Press, 2017. p. 319-326.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Adaptive spammer detection with sparse group modeling
AU - Wu, Liang
AU - Hu, Xia
AU - Morstatter, Fred
AU - Liu, Huan
PY - 2017
Y1 - 2017
N2 - Social spammers disseminate unsolicited information on social media sites that negatively impacts social networking systems. To detect social spammers, traditional methods leverage social network structures to identify the behavioral patterns hidden in their social interactions. They focus on accounts that are affiliated with groups comprising known spammers. However, since different parties are emerging to generate various spammers, they may form different kinds of groups, and some spammers may even detach from the flock. Therefore, it is challenging for existing methods to find the optimal group structure that captures different spammers simultaneously. Employing different approaches for specific spammers is time-consuming, and it also lacks the adaptivity of dealing with emerging spammers. In this work, we aim to propose a group modeling framework that adaptively characterizes social interactions of spammers. In particular, we introduce to integrate content information into the group modeling process. The proposed framework exploits additional content information in selecting groups and individuals that are likely to be involved in spamming activities. In order to alleviate the intensive computational cost, we transform the problem as a sparse learning task that can be solved efficiently. Experimental results on realworld datasets show that the proposed method outperforms the state-of-the-art approaches.
AB - Social spammers disseminate unsolicited information on social media sites that negatively impacts social networking systems. To detect social spammers, traditional methods leverage social network structures to identify the behavioral patterns hidden in their social interactions. They focus on accounts that are affiliated with groups comprising known spammers. However, since different parties are emerging to generate various spammers, they may form different kinds of groups, and some spammers may even detach from the flock. Therefore, it is challenging for existing methods to find the optimal group structure that captures different spammers simultaneously. Employing different approaches for specific spammers is time-consuming, and it also lacks the adaptivity of dealing with emerging spammers. In this work, we aim to propose a group modeling framework that adaptively characterizes social interactions of spammers. In particular, we introduce to integrate content information into the group modeling process. The proposed framework exploits additional content information in selecting groups and individuals that are likely to be involved in spamming activities. In order to alleviate the intensive computational cost, we transform the problem as a sparse learning task that can be solved efficiently. Experimental results on realworld datasets show that the proposed method outperforms the state-of-the-art approaches.
UR - http://www.scopus.com/inward/record.url?scp=85029437735&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85029437735&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85029437735
SP - 319
EP - 326
BT - Proceedings of the 11th International Conference on Web and Social Media, ICWSM 2017
PB - AAAI Press
ER -