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TrustyTweet: An Indicator-based Browser-Plugin to Assist Users in Dealing with Fake News on Twitter

Hartwig, Katrin ; Reuter, Christian (2022)
TrustyTweet: An Indicator-based Browser-Plugin to Assist Users in Dealing with Fake News on Twitter.
14. Internationale Tagung Wirtschaftsinformatik (WI 2019). Siegen, Germany (23.-27.2.2019)
doi: 10.26083/tuprints-00020747
Conference or Workshop Item, Secondary publication, Publisher's Version

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Item Type: Conference or Workshop Item
Type of entry: Secondary publication
Title: TrustyTweet: An Indicator-based Browser-Plugin to Assist Users in Dealing with Fake News on Twitter
Language: English
Date: 2022
Place of Publication: Darmstadt
Year of primary publication: 2019
Publisher: Association for Information Systems AIS
Book Title: Tagungsband WI 2019 : Human Practice. Digital Ecologies. Our Future.
Event Title: 14. Internationale Tagung Wirtschaftsinformatik (WI 2019)
Event Location: Siegen, Germany
Event Dates: 23.-27.2.2019
DOI: 10.26083/tuprints-00020747
Corresponding Links:
Origin: Secondary publication service

The importance of dealing withfake newsonsocial mediahas increased both in political and social contexts.While existing studies focus mainly on how to detect and label fake news, approaches to assist usersin making their own assessments are largely missing. This article presents a study on how Twitter-users’assessmentscan be supported by an indicator-based white-box approach.First, we gathered potential indicators for fake news that have proven to be promising in previous studies and that fit our idea of awhite-box approach. Based on those indicators we then designed and implemented the browser-plugin TrusyTweet, which assists users on Twitterin assessing tweetsby showing politically neutral and intuitive warnings without creating reactance. Finally, we suggest the findings of our evaluations with a total of 27 participants which lead to further design implicationsfor approachesto assistusers in dealing with fake news.

Uncontrolled Keywords: Fake News, Social Media, Twitter, Plugin
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-207476
Classification DDC: 000 Generalities, computers, information > 004 Computer science
300 Social sciences > 380 Commerce, communications, transportation
Divisions: 20 Department of Computer Science > Science and Technology for Peace and Security (PEASEC)
Profile Areas > Cybersecurity (CYSEC)
LOEWE > LOEWE-Zentren > CRISP - Center for Research in Security and Privacy
Zentrale Einrichtungen > Interdisziplinäre Arbeitsgruppe Naturwissenschaft, Technik und Sicherheit (IANUS)
Date Deposited: 15 Dec 2022 12:42
Last Modified: 24 Mar 2023 14:15
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/20747
PPN: 503376701
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