Behavioural Based Online Comment Spammers in Social Media

Abstract

With the development of mobile Internet, it is changing the way we communicate with others. Internet media plays important application for information dissemination and user communication, including online news and social networks. However, the present advancement in technology provides opportunities to raise large number of spammers, who release false speech, advertisements and phishing websites on the media to gain commercial benefits, which seriously affects the experience of normal users. Therefore, in order to reduce the harm of false information, our paper focuses on the identification of spammers from normal users. However, the existing technologies of identifying spammers involve high data costs and poor effects, and most of them are concentrated in the field of social networks, while less research is carried out in the field of online news. In this paper, we propose an effective technology of identifying online news comment spammers based on the comment propagation algorithm (CPA), making full use of the user comment behaviours and contents. First of all, we extract few amount of information using scraping tool and label some users in the data as spammers or normal users manually to construct a labelled dataset. Next, we propose the identification technology based on the CPA. Finally, the set of values is input into the proposed technology in different combinations, and experiments and evaluations are carried out to determine possible spammers using behavioural features.