Abstract
Online websites have become an important source of information in all domains. Health has become one of the most Internet-dependent domains for information for the common users and experts alike. However, health information can be a critical determinant of human health and false information may cause real harm to Internet users. In this research, we aim to develop a model that evaluates the degree of trust in websites that provide health information. We conducted a quasi-experiment to assess the factors that affect user trust in health information providing websites. The experiment was conducted on pre-selected websites that provided information on Covid-19, ranging from official sources to those reported as providing misinformation. Participants had to assess the websites and determine factors that affected their level of trust. A total of 30 participated in the quasi-experiment, including both common users (46%) and health experts (56%). As a result, we identified the user-perceived importance weight of each of the studied factors that affect user trust in the studied websites. Using the identified importance weights of the factors, we developed a trust model and algorithm to evaluate the degree of trust in websites that provide health information. To evaluate the scalability of the developed model and algorithm, they were additionally applied on a set of pre-identified websites. The results were compared to the manually assessed scores conducted by health expert participants. The developed model achieved an error rate between 15%–19%, depending on the type and nature of the information-providing websites.
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Alajramy, L., Taweel, A. (2022). User Perception Based Trust Model of Online Sources: A Case Study of Misinformation on COVID-19. In: Spezzano, F., Amaral, A., Ceolin, D., Fazio, L., Serra, E. (eds) Disinformation in Open Online Media. MISDOOM 2022. Lecture Notes in Computer Science, vol 13545 . Springer, Cham. https://doi.org/10.1007/978-3-031-18253-2_1
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