Spam Detection and Analysis in e-WOM Communities (Master AI)

e-WOM systems offer the user assistance with his buying decision. However, like most systems that involve money, e-WOM communities suffer from untruthful opinion spam, indented to push the own products or criticise the competitor. Several scholars proposed algorithms to detect such reviews. These systems are not perfect; under their artificial conditions they provide a detection rate slightly better than a random guess. This work proposes a community driven mechanism to incorporate already proposed systems in a UI, to let the users decide, aided by the system, whether a review is opinion spam or not.


Werner Gaulke

Former team member


Related focus areas