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Computer Science > Human-Computer Interaction

arXiv:2109.12523 (cs)
[Submitted on 26 Sep 2021 (v1), last revised 26 Apr 2022 (this version, v2)]

Title:A Study of Fake News Reading and Annotating in Social Media Context

Authors:Jakub Simko, Patrik Racsko, Matus Tomlein, Martin Hanakova, Robert Moro, Maria Bielikova
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Abstract:The online spreading of fake news is a major issue threatening entire societies. Much of this spreading is enabled by new media formats, namely social networks and online media sites. Researchers and practitioners have been trying to answer this by characterizing the fake news and devising automated methods for detecting them. The detection methods had so far only limited success, mostly due to the complexity of the news content and context and lack of properly annotated datasets. One possible way to boost the efficiency of automated misinformation detection methods, is to imitate the detection work of humans. It is also important to understand the news consumption behavior of online users. In this paper, we present an eye-tracking study, in which we let 44 lay participants to casually read through a social media feed containing posts with news articles, some of which were fake. In a second run, we asked the participants to decide on the truthfulness of these articles. We also describe a follow-up qualitative study with a similar scenario but this time with 7 expert fake news annotators. We present the description of both studies, characteristics of the resulting dataset (which we hereby publish) and several findings.
Subjects: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY); Machine Learning (cs.LG); Social and Information Networks (cs.SI)
ACM classes: H.5.2; H.5.4; K.4.2; H.3.1
Cite as: arXiv:2109.12523 [cs.HC]
  (or arXiv:2109.12523v2 [cs.HC] for this version)
  https://doi.org/10.48550/arXiv.2109.12523
Journal reference: New Review of Hypermedia and Multimedia. pages 1-31 (2021)
Related DOI: https://doi.org/10.1080/13614568.2021.1889691

Submission history

From: Maria Bielikova [view email]
[v1] Sun, 26 Sep 2021 08:11:17 UTC (2,659 KB)
[v2] Tue, 26 Apr 2022 19:08:18 UTC (2,659 KB)
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