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Tweet, but verify: epistemic study of information verification on Twitter

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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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  5. We filter by language of the source tweet, because that is the tweet we show during the surveys

  6. Retweets were identified as tweets containing the field “retweeted_status” pointing to the original tweet.

  7. e.g.,


  9. with the exception that corroboration is not always available


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We would like to thank the anonymous reviewers for providing us with constructive comments and suggestions. This work was supported by the US Army Research Laboratory under Cooperative Agreement No. W911NF-09-2-0053 (NS-CTA), US NSF CAREER Award under Grant IIS-0953149, US DARPA Award No. FA8750-13-2-0041 in the “Deep Exploration and Filtering of Text” (DEFT) Program and Rensselaer Polytechnic Institute Start-up fund for Heng Ji. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the social policies, either expressed or implied, of the US Government. The US Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.

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Correspondence to Arkaitz Zubiaga.

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This article is part of the Topical Collection on Uncovering Deception in Social Media.

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Zubiaga, A., Ji, H. Tweet, but verify: epistemic study of information verification on Twitter. Soc. Netw. Anal. Min. 4, 163 (2014).

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