Paper: Deceptive Answer Prediction with User Preference Graph

ACL ID P13-1169
Title Deceptive Answer Prediction with User Preference Graph
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013

In Community question answering (QA) sites, malicious users may provide decep- tive answers to promote their products or services. It is important to identify and fil- ter out these deceptive answers. In this paper, we first solve this problem with the traditional supervised learning meth- ods. Two kinds of features, including tex- tual and contextual features, are investi- gated for this task. We further propose to exploit the user relationships to identify the deceptive answers, based on the hy- pothesis that similar users will have simi- lar behaviors to post deceptive or authentic answers. To measure the user similarity, we propose a new user preference graph based on the answer preference expressed by users, such as ?helpful? voting and ?best answer? selection. The user prefer- ence g...