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Title: FeedReflect: A Tool for Nudging Users to Assess News Credibility on Twitter
In recent years, the emergence of fake news outlets has drawn out the importance of news literacy. This is particularly critical in social media where the flood of information makes it difficult for people to assess the veracity of the false stories from such deceitful sources. Therefore, people oftentimes fail to look skeptically at these stories. We explore a way to circumvent this problem by nudging users into making conscious assessments of what online contents are credible. For this purpose, we developed FeedReflect, a browser extension. The extension nudges users to pay more attention and uses reflective questions to engage in news credibility assessment on Twitter. We recruited a small number of university students to use this tool on Twitter. Both qualitative and quantitative analysis of the study suggests the extension helped people accurately assess the credibility of news. This implies FeedReflect can be used for the broader audience to improve online news literacy.
Authors:
; ; ; ;
Award ID(s):
1755547 2041068
Publication Date:
NSF-PAR ID:
10082960
Journal Name:
CSCW '18 Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing
Page Range or eLocation-ID:
205 to 208
Sponsoring Org:
National Science Foundation
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