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      Tweet, but Verify: Epistemic Study of Information Verification on Twitter

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          Abstract

          While Twitter provides an unprecedented opportunity to learn about breaking news and current events as they happen, it often produces skepticism among users as not all the information is accurate but also hoaxes are sometimes spread. While avoiding the diffusion of hoaxes is a major concern during fast-paced events such as natural disasters, the study of how users trust and verify information from tweets in these contexts has received little attention so far. We survey users on credibility perceptions regarding witness pictures posted on Twitter related to Hurricane Sandy. By examining credibility perceptions on features suggested for information verification in the field of Epistemology, we evaluate their accuracy in determining whether pictures were real or fake compared to professional evaluations performed by experts. Our study unveils insight about tweet presentation, as well as features that users should look at when assessing the veracity of tweets in the context of fast-paced events. Some of our main findings include that while author details not readily available on Twitter feeds should be emphasized in order to facilitate verification of tweets, showing multiple tweets corroborating a fact misleads users to trusting what actually is a hoax. We contrast some of the behavioral patterns found on tweets with literature in Psychology research.

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          Most cited references 4

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          How do consumers search for and appraise health information on the world wide web? Qualitative study using focus groups, usability tests, and in-depth interviews

           G Eysenbach (2002)
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            Information credibility on twitter

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              Wartime rumors of waste and special privilege: why some people believe them.

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                Author and article information

                Journal
                18 December 2013
                2013-12-29
                Article
                1312.5297

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                Custom metadata
                Pre-print of paper accepted to Social Network Analysis and Mining (Springer)
                cs.SI cs.CY

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