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Erschienen in: Social Network Analysis and Mining 1/2014

01.12.2014 | Original Article

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

verfasst von: Arkaitz Zubiaga, Heng Ji

Erschienen in: Social Network Analysis and Mining | Ausgabe 1/2014

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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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Fußnoten
1
https://en.wikipedia.org/wiki/Hurricane_Sandy
 
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.
 
9
with the exception that corroboration is not always available
 
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Metadaten
Titel
Tweet, but verify: epistemic study of information verification on Twitter
verfasst von
Arkaitz Zubiaga
Heng Ji
Publikationsdatum
01.12.2014
Verlag
Springer Vienna
Erschienen in
Social Network Analysis and Mining / Ausgabe 1/2014
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI
https://doi.org/10.1007/s13278-014-0163-y

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