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

Hartwig, Katrin ; Reuter, Christian (2019)
TrustyTweet: An Indicator-based Browser-Plugin to Assist Users in Dealing with Fake News on Twitter.
14th Interantional Conference on Wirtschaftsinformatik. Siegen, Germany (23.02.2019-27.02.2019)
Konferenzveröffentlichung, Bibliographie

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Kurzbeschreibung (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.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2019
Autor(en): Hartwig, Katrin ; Reuter, Christian
Art des Eintrags: Bibliographie
Titel: TrustyTweet: An Indicator-based Browser-Plugin to Assist Users in Dealing with Fake News on Twitter
Sprache: Englisch
Publikationsjahr: 2019
Ort: Siegen, Germany
Verlag: Association for Information Systems AIS
Buchtitel: Wirtschaftsinformatik Proceedings 2019
Veranstaltungstitel: 14th Interantional Conference on Wirtschaftsinformatik
Veranstaltungsort: Siegen, Germany
Veranstaltungsdatum: 23.02.2019-27.02.2019
URL / URN: https://aisel.aisnet.org/wi2019/specialtrack01/papers/5/
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Kurzbeschreibung (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.

Freie Schlagworte: CRISP, Frieden, HCI, SocialMedia
Zusätzliche Informationen:

Erstveröffentlichung

Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Wissenschaft und Technik für Frieden und Sicherheit (PEASEC)
Profilbereiche
Profilbereiche > Cybersicherheit (CYSEC)
LOEWE
LOEWE > LOEWE-Zentren
LOEWE > LOEWE-Zentren > CRISP - Center for Research in Security and Privacy
Zentrale Einrichtungen
Zentrale Einrichtungen > Interdisziplinäre Arbeitsgruppe Naturwissenschaft, Technik und Sicherheit (IANUS)
Hinterlegungsdatum: 26 Aug 2019 07:26
Letzte Änderung: 03 Jul 2024 02:39
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