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

  • Arkaitz Zubiaga
  • Heng Ji
Original Article
Part of the following topical collections:
  1. Uncovering Deception in Social Media


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.


Real Picture Twitter User Information Credibility Information Verification Author Detail 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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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Copyright information

© Springer-Verlag Wien 2014

Authors and Affiliations

  1. 1.Computer Science Department, Queens College and Graduate Center City University of New YorkNew YorkUSA
  2. 2.Computer Science DepartmentRensselaer Polytechnic InstituteTroyUSA

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