Efeosasere Moibi Okoro, Benjamin Abaioni Abara, Aneyelewa Alan-Ajonye, Zayyad Isa, Alex Umagba


Abstract: The detection of fake news is a relevant problem-solving mechanism. Literature presents us with three methods of detecting fake news; Human-based, Machine-based and the Human-Machine (hybrid) method. There are questions about which detection approach may be best in detecting fake news. In this study, we test the effects of using two existing methods to detect fake news: Human-based and the Human-Machine approach. Participants perform a small classification task where they were asked to determine if a news article was fake or not. The study used two levels of a within-subject design; where each participant used both the human-based approach and the human-machine approach. Performance and User experience were the dependent variables. Results of the study show that the Human-Machine approach improved user fake news detection effectiveness by 26%. The results suggest that augmenting human intelligence with machines has benefits in fake news detection.   


Fake news detection; Human-Machine Collaboration; Detection Performance; Fake news; Human Factor

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DOI: https://doi.org/10.26483/ijarcs.v10i1.6358


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