AAAI Publications, Fifth AAAI Conference on Human Computation and Crowdsourcing

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Crowd-O-Meter: Predicting if a Person Is Vulnerable to Believe Political Claims
Mehrnoosh Sameki, Tianyi Zhang, Linli Ding, Margrit Betke, Danna Gurari

Last modified: 2017-09-21

Abstract


Social media platforms have been criticized for promoting false information during the 2016 U.S. presidential election campaign. Our work is motivated by the idea that a platform could reduce the circulation of false information if it could estimate whether its users are vulnerable to believing political claims. We here explore whether such a vulnerability could be measured in a crowdsourcing setting. We propose Crowd-O-Meter, a framework that automatically predicts if a crowd worker will be consistent in his/her beliefs about political claims; i.e., consistently believes the claims are true or consistently believes the claims are not true. Crowd-O-Meter is a user-centered approach which interprets a combination of cues characterizing the user's implicit and explicit opinion bias. Experiments on 580 quotes from PolitiFact's fact checking corpus of 2016 U.S. presidential candidates show that Crowd-O-Meter is precise and accurate for two news modalities: text and video. Our analysis also reveals which are the most informative cues of a person's vulnerability.

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