Enhancing Identification of Opinion Spammer Groups

Consumer-generated media buzz has significant influence on brands and is also the reason that has forced companies to communicate with consumers through consumer-generated channels. Recent investigations about brand recognition however show that many prominent brands are using fake followers or fake opinions for their promotion or to discredit their opponents. It is of paramount importance to detect such fake activities to ensure that the Web remains a trusted source of valuable information. So far, it is still quite easy to detect fake followers. On the other hand fake opinions are very hard to recognize as fake ones by just reading them. Most of the research in the field of fake opinions has been targeting the detection of fake opinions or reviews originating from single opinion spammers. Since groups of opinion spammers can simply mislead the most sophisticated algorithms that focus on content of the fake opinion we propose a novel idea to enhance the detection of fake profiles based on content originated by such profiles using additional features from quantitative psycho-linguistic text analytics tools.