How Fake News Affect Trust in the Output of a Machine Learning System for News Curation

  title={How Fake News Affect Trust in the Output of a Machine Learning System for News Curation},
  author={H. Heuer and A. Breiter},
  • H. Heuer, A. Breiter
  • Published in MISDOOM 2020
  • Computer Science
  • People are increasingly consuming news curated by machine learning (ML) systems. Motivated by studies on algorithmic bias, this paper explores which recommendations of an algorithmic news curation system users trust and how this trust is affected by untrustworthy news stories like fake news. In a study with 82 vocational school students with a background in IT, we found that users are able to provide trust ratings that distinguish trustworthy recommendations of quality news stories from… CONTINUE READING

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    Publications referenced by this paper.
    Social Media and Fake News in the 2016 Election
    • 1,663
    • PDF
    Is seeing believing?: how recommender system interfaces affect users' opinions
    • 463
    Understanding User Beliefs About Algorithmic Curation in the Facebook News Feed
    • 133
    • PDF
    The science of fake news
    • 650
    Explaining collaborative filtering recommendations
    • 1,440
    • Highly Influential
    • PDF
    GroupLens: an open architecture for collaborative filtering of netnews
    • 5,399
    • PDF
    The Adressa dataset for news recommendation
    • 32
    Trust in recommender systems
    • 855
    • PDF
    How to Recommend?: User Trust Factors in Movie Recommender Systems
    • 32