Cited by 27Misinformation and Its ImpactsJournalism and Media StudiesCommunication and COVID-19 Impact

Trust in news on social media

Heuer, Hendrik, Breiter, Andreas·2018·Proceedings of the 10th Nordic Conference on Human-Computer Interaction

Abstract

This paper investigates trust in news on a social media platform. The paper is motivated by the finding that social media is the primary news source for a large group of people, especially young adults. Considering the challenges posed by online misinformation and fake news, an understanding of how users quantify trust in news and what factors influence this trust is needed. In a study with 108 participants, German high-school students provided trust ratings for online news including quality media and fake news. The study shows that users can quantify their trust in news items and that these trust ratings correspond to rankings of the sources by experts. The paper finds that psychometric scales that measure interpersonal trust are predictive of a user's mean trust rating across different news items. We show how this can be used to provide interventions for those prone to false trust and false distrust.

Integrity signals

Registry

Crossref · 10.1145/3240167.3240172

Cites

33 of 33 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Prior experiences with and attitudes towards the trustee are essential antecedents of trust in information found on social media.

    “People's prior experiences with and attitudes towards the trustee infuence their perceptions of that trustee and have been shown to be essential antecedents of trust in information found on social media [5,31,32,40,122].”
  • Multi-media and visual components invoke positive emotional sentiments among users, which contributes to increased trust in that information.

    “This is because these components often invoke positive emotional sentiments among users, which contributes to increased trust in that information [13,40,56].”
  • Presenting information alongside the URL of the original source significantly increases people's trust in that information.

    “In a number of studies, transparency has involved presenting information alongside the URL of the original source, which signifcantly increased people's trust in that information [5,13,24,25,40,109].”
  • In the experimental study [40], participants' general belief in trusting others can predict their trust rating of a Facebook post.

    “The authors found that participants' general belief on trusting others can predict their trust rating of a Facebook post, and suggested interventions for those prone to false trust and false distrust.”

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Heuer, H., & Breiter, A.
Affiliations
University of Bremen (DE)
Venue
Proceedings of the 10th Nordic Conference on Human-Computer Interaction
Publisher
ACM
Published
2018-09-29
Pages
137-147
OpenAlex
W2892401976
Cited by
27
References
33
Citations by year
2026: 2 · 2025: 3 · 2024: 3 · 2023: 3 · 2022: 3 · 2021: 7 · 2020: 2 · 2019: 4
FWCI
9.90
Type
conference paper
Language
en
Open access
closed
Locations
Nordic Conference on Human-Computer Interaction (publishedVersion)
Topics
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Social Sciences › Communication › Journalism and Media Studies
  • Social Sciences › Communication › Communication and COVID-19 Impact
Keywords
trust in news, social media, fake news, misinformation, online news sources, trust ratings, interpersonal trust, psychometric scale, young adults, high school students

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review