Modelling users' trust in online health rumours: an experiment-based study in China

By Shengli Deng, Shaoxiong Fu, Yong Liu, Hongxiu Li. Information Research, 2021

334 citations recorded by the source corpus.

Introduction. With the increasing availability of information on the Internet, online rumours have become prevalent, and it is not uncommon for search engines to return unverified rumours about health. However, false information in such domains may lead to serious consequences if it gains users' trust. An understanding of the characteristics of online health rumours that users' trust is important for fighting their spread. Method. Using real-world online health rumour data from a Chinese database, the authors investigated the predictors of users' trust in online health rumours. An experiment (n = 30) and interviews (n = 10) were conducted to examine how users evaluate particular types of health rumours. Analysis. The effects of rumours' manner of presentation and the perceived information quality on users' trust were tested using ANOVA (with SPSS software) for the quantitative data collected in the experiment. The qualitative component applied content analysis of the interview data to further explain the results produced by the quantitative analysis. Results. The impact of pictures (one dimension of rumour presentation) on users' trust varies, depending on the perceived quality of the pictures displayed with the online health rumours, and informativeness (a dimension of information quality) is an influential predictor of trusting beliefs. Conclusions. The paper serves the aim of highly effective prediction of users' trust in online health rumours, and it contributes new insights for proactively evaluating the hazard level of a particular online health-rumour item.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/2dc32d2a1e4b80c3c057e01a46a4a62b6a1e83b1

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In Artificial Intelligence

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