An Implicit Crowdsourcing Approach to Rumor Identification in Online Social Networks

Abstract

With the increasing use of online social networks as a source of news and information, the propensity for a rumor to disseminate widely and quickly poses a great concern, especially in disaster situations where users do not have enough time to fact-check posts before making the informed decision to react to a post that appears to be credible. At the same time, we know that misinformation is easily detectable by a certain few, very skeptical, or very informed users. In this study, we demonstrate how blending artificial intelligence and human skills can create a new paradigm for credibility prediction. The crowdsourcing part of the detection mechanism is implemented implicitly, by simply observing the natural interaction between users encountering the messages. Specifically, we explore the spread of information on Twitter at the microscopic (user-to-user propagation) level and propose a model that predicts if a message is True or False by observing the latent attributes of the message, along with those of the users interacting with it, and their reactions to the message. We demonstrate the application of this model to the detection of misinformation and rank the relevant message and user features that are most critical in influencing the spread of rumor over the network. Our experiments using real-world data show that the proposed model achieves over 90% accuracy in predicting the credibility of posts on Twitter, a significant boost over state-of-the-art models.