Exploiting emotional information for trust/distrust prediction

Trust and distrust networks are usually extremely sparse and the vast majority of the existing algorithms for trust/distrust prediction suffer from the data sparsity problem. In this paper, following the research from psychology and sociology, we envision that users' emotions such as happiness and anger are strong indicators of trust/distrust relations. Meanwhile the popularity of social media encourages the increasing number of users to freely express their emotions; hence emotional information is pervasively available and usually denser than the trust and distrust relations. Therefore incorporating emotional information could have the potentials to alleviate the data sparsity in the problem of trust/distrust prediction. In this study, we investigate how to exploit emotional information for trust/distrust prediction. In particular, we provide a principled way to capture emotional information mathematically and propose a novel trust/distrust prediction framework ETD. Experimental results on the real-world social media dataset demonstrate the effectiveness of the proposed framework and the importance of emotional information in trust/distrust prediction.