Rumor has it: Identifying Misinformation in Microblogs

Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pages 1589–1599, Edinburgh, Scotland, UK, July 27–31, 2011. c⃝2011 Association for Computational Linguistics Rumor has it: Identifying Misinformation in Microblogs Vahed Qazvinian Emily Rosengren Dragomir R. Radev Qiaozhu Mei University of Michigan Ann Arbor, MI {vahed,emirose,radev,qmei}@umich.edu 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 Introduction A rumor is an unverified and instrumentally relevant statement of information spread among people (Di- Fonzo and Bordia, 2007). Social psychologists ar- gue that rumors arise in contexts of ambiguity, when the meaning of a situation is not readily apparent, or potential threat, when people feel an acute need for security. For instance rumors about ‘office ren- ovation in a company’ is an example of an ambigu- ous context, and the rumor that ‘underarm deodor- ants cause breast cancer’ is an example of a context in which one’s well-being is at risk (DiFonzo et al., 1994). The rapid growth of online social media has made it possible for rumors to spread more quickly. On- line social media enable unreliable sources to spread large amounts of unverified information among peo- ple (Herman and Chomsky, 2002). Therefore, it is crucial to design systems that automatically detect misinformation and disinformation (the former of- ten seen as simply false and the latter as deliberately false information). Our definition of a rumor is established based on social psychology, where a rumor is defined as a statement whose truth-value is unverifiable or delib- erately false. In-depth rumor analysis such as deter- mining the intent and impact behind the spread of a rumor is a very challenging task and is not possi- ble without first retrieving the complete set of social conversations (e.g., tweets) that are actually about the rumor. In our work, we take this first step to retrieve a complete set of tweets that discuss a spe- cific rumor. In our approach, we address two basic problems. The first problem concerns retrieving on- line microblogs that are rumor-related. …

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