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Detecting spammers in community question answering (2013)
| Venue: | In Proceeding of International Joint Conference on Natural Language Processing |
| Citations: | 1 - 0 self |
BibTeX
@INPROCEEDINGS{Ding13detectingspammers,
author = {Zhuoye Ding and Yeyun Gong and Yaqian Zhou and Qi Zhang and Xuanjing Huang},
title = {Detecting spammers in community question answering},
booktitle = {In Proceeding of International Joint Conference on Natural Language Processing},
year = {2013},
pages = {118--126}
}
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Abstract
As the popularity of Community Ques-tion Answering(CQA) increases, spam-ming activities also picked up in number-s and variety. On CQA sites, spammers often pretend to ask questions, and selec-t answers which were published by their partners or themselves as the best answers. These fake best answers cannot be easily detected by neither existing methods nor common users. In this paper, we address the issue of detecting spammers on CQA sites. We formulate the task as an opti-mization problem. Social information is incorporated by adding graph regulariza-tion constraints to the text-based predic-tor. To evaluate the proposed approach, we crawled a data set from a CQA portal. Experimental results demonstrate that the proposed method can achieve better per-formance than some state-of-the-art meth-ods. 1
