Tribalism and Fake News: Descriptive and Predictive Models on How Belief Influences News Trust

Abstract

There are studies that have investigated the perception or the impact of trusting fake news. There are also articles describing how divisions in society arise and what the consequences are. However, there are few studies that have looked at the divisiveness of society on social networks and how it manifests itself in trust in (fake) news. The problem our research would like to address is how fake news and the so-called tribalism are connected. We set out too see whether people tend to seek for information that validates their current belief, even if that information is untrue, rather than seeking for the truth. Based on existing research, we created a questionnaire that combined demographic questions, questions about trust, the big five factors, a quiz where a person was asked to spot the fake news and questions that asked to determine the tribe of an individual. We also set up a website that mimicked currently popular social networks. Using this, we recorded users' actions, which was an integral part of the individual's participation in this research. The total number of respondents was 138, 69 men and 69 women, mostly from Slovenia and elsewhere in Europe, but also from Asia and North America. The data were cleaned, normalised, factorised and processed. We used various techniques to create new features from the existing data, which helped us in the next step. This was to set up various models in order to obtain the highest possible level of prediction accuracy through nested cross-validation. The experiments we carried out shows that, based on an individual's behaviour on a social network, it is possible to determine which tribe he or she belongs to and which news stories they will believe. The results also show that exploring social science questions using machine learning has great potential for future work. © 2022 Elsevier B.V., All rights reserved.