Zubiaga, Arkaitz and Ji, Heng (2014) Tweet, but verify : epistemic study of information verification on Twitter. Social Network Analysis and Mining, Volume 4 (Number 1). Article number 63. doi:10.1007/s13278-014-0163-y

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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.

Item Type:	Journal Article
Subjects:	B Philosophy. Psychology. Religion > BF Psychology P Language and Literature > P Philology. Linguistics
Divisions:	Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH):	Online social networks -- Psychological aspects, Twitter
Journal or Publication Title:	Social Network Analysis and Mining
Publisher:	Springer Wien
ISSN:	1869-5450
Official Date:	25 March 2014
Dates:	Date Event 25 March 2014 Published
Date of first compliant deposit:	18 July 2016
Volume:	Volume 4
Number:	Number 1
Article Number:	Article number 63
DOI:	10.1007/s13278-014-0163-y
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Funder:	U.S. Army Research Laboratory (ARL), United States. Defense Advanced Research Projects Agency (DARPA), National Science Foundation (U.S.) (NSF), Rensselaer Polytechnic Institute
Grant number:	W911NF-09-2-0053 (ARL), IIS-0953149 (NSF), FA8750-13-2-0041 (DARPA)
Embodied As:	1

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