Efficient Stance Ordering to Improve Rumor Veracity Detection

Social media is an inseparable part of human life, although published information through social media is not always true. Rumors may spread easily and quickly in the social media, hence, it is vital to have a tool for rumor veracity detection. Papers already proved that users’ stance is an important tool for this goal. To the best knowledge of authors, so far, no work has been proposed to study the ordering of users’ stances to achieve the best possible accuracy. In this work, we have investigated the importance of the stances ordering in the efficiency of rumor veracity detection. This paper introduces a concept called trust for stance sequence ordering and shows that proper definition of this function can significantly help improve to improve veracity detection. The paper examines and compares different modes of definition of trust. Then, by choosing the best possible definition, it was able to outperform state-of-the-art results on a well-known dataset in this field, namely SemEval 2019.

Efficient Stance Ordering to Improve Rumor Veracity Detection Keywords:

Stance , rumor detection , rumor veracity , ordering , deep learning