- Sardar Hamidian, Mona T. Diab. 2016. Rumor Identification and Belief Investigation on Twitter. Abstract: Social media users spend several hours a day to read, post and search for news on microblogging platforms. Social media is becoming a key means for discovering news. However, verifying the trustworthiness of this information is becoming even more challenging. In this study, we attempt to address the problem of rumor detection and belief investigation on Twitter. Our deﬁnition of rumor is an unveriﬁable statement, which spreads mis-information or disinformation. We adopt a supervised rumors classiﬁcation task using the standard dataset. By employing the Tweet Latent Vector (TLV) feature, which creates a 100-d vector representative of each tweet, we increased the rumor retrieval task precision up to 0.972. We also introduce the belief score and study the belief change among the rumor posters between 2010 and 2016.
