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 (23.02.2019-27.02.2019)
doi: 10.26083/tuprints-00020747
Conference or Workshop Item, Secondary publication, Publisher's Version
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Item Type: | Conference or Workshop Item |
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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: | 23.02.2019-27.02.2019 |
DOI: | 10.26083/tuprints-00020747 |
Corresponding Links: | |
Origin: | Secondary publication service |
Abstract: | 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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