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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.-27.02.2019)
Conference or Workshop Item, Bibliographie

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.

Item Type: Conference or Workshop Item
Erschienen: 2019
Creators: Hartwig, Katrin ; Reuter, Christian
Type of entry: Bibliographie
Title: TrustyTweet: An Indicator-based Browser-Plugin to Assist Users in Dealing with Fake News on Twitter
Language: English
Date: 2019
Place of Publication: Siegen, Germany
Publisher: Association for Information Systems AIS
Book Title: Wirtschaftsinformatik Proceedings 2019
Event Title: 14th Interantional Conference on Wirtschaftsinformatik
Event Location: Siegen, Germany
Event Dates: 23.-27.02.2019
URL / URN: https://aisel.aisnet.org/wi2019/specialtrack01/papers/5/
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: CRISP, Frieden, HCI, SocialMedia
Additional Information:

Erstveröffentlichung

Divisions: 20 Department of Computer Science
20 Department of Computer Science > Science and Technology for Peace and Security (PEASEC)
Profile Areas
Profile Areas > Cybersecurity (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)
Date Deposited: 26 Aug 2019 07:26
Last Modified: 02 Jan 2023 09:12
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