Rumor has it: Identifying Misinformation in Microblogs

Abstract

A rumor is commonly defined as a state-ment whose true value is unverifiable. Ru-mors may spread misinformation (false infor-mation) or disinformation (deliberately false information) on a network of people. Identi-fying rumors is crucial in online social media where large amounts of information are easily spread across a large network by sources with unverified authority. In this paper, we address the problem of rumor detection in microblogs and explore the effectiveness of 3 categories of features: content-based, network-based, and microblog-specific memes for correctly iden-tifying rumors. Moreover, we show how these features are also effective in identifying disin-formers, users who endorse a rumor and fur-ther help it to spread. We perform our exper-iments on more than 10,000 manually anno-tated tweets collected from Twitter and show how our retrieval model achieves more than 0.95 in Mean Average Precision (MAP). Fi-nally, we believe that our dataset is the first large-scale dataset on rumor detection. It can open new dimensions in analyzing online mis-information and other aspects of microblog conversations. 1