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The Power of Related Articles : Improving Fake News Detection on Social Media Platforms

Title data

Gimpel, Henner ; Heger, Sebastian ; Kasper, Julia ; Schäfer, Ricarda:
The Power of Related Articles : Improving Fake News Detection on Social Media Platforms.
2020
Event: 53th Hawaii International Conference on System Sciences (HICSS) , 07.-10.01.2020 , Wailea , USA.
(Conference item: Conference , Speech )

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Abstract in another language

Social media is increasingly used as a platform for news consumption, but it has also become a breeding ground for intentionally false information, known as fake news. This serious threat poses significant challenges to social media providers, society, and science. To date, several studies have investigated automated approaches to fighting fake news, but the problem has yet to be solved. Little work has been done, however, to improve fake news detection on the users’ side. A simple but prom-ising approach is to broaden users' knowledge in order to improve the perceptual process, which will improve detection behavior. This study evaluates the impact of a digital nudging approach which aims to fight fake news with the help of related articles. It also considers the effect of three personal-ity traits: anxiety, conscientiousness, and curiosity. 322 participants took part in an online experi-ment simulating the Facebook Newsfeed. In addition to a control group, three treatment groups were exposed to different combinations of related articles. Results indicate that the presence of controver-sial related articles has a positive influence on the detection of fake news.

Further data

Item Type: Conference item (Speech)
Refereed: Yes
Keywords: Social Media; Fake News; Detection; Digital Nudging; Experiment
Institutions of the University: Faculties > Faculty of Law, Business and Economics > Department of Business Administration
Research Institutions
Research Institutions > Affiliated Institutes
Research Institutions > Affiliated Institutes > FIM Research Center Finance & Information Management
Result of work at the UBT: No
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 330 Economics
Date Deposited: 26 Sep 2019 07:33
Last Modified: 26 Sep 2019 07:33
URI: https://eref.uni-bayreuth.de/id/eprint/52436