Dataset Information

Fake news spreader detection using trust-based strategies in social networks with bot filtration.

ABSTRACT: An important aspect of preventing fake news spreading in social networks is to proactively detect the users that are likely going to spread such news. Research in the domain of spreader detection is at a nascent stage compared to fake news detection. In this paper, we propose a graph neural network-based framework to identify nodes that are likely to become spreaders of false information. Using the community health assessment model and interpersonal trust (quantified using network topology and historical behavioral data), we propose an inductive representation learning framework to predict nodes of densely connected community structures that are most likely to spread fake news, thus making the entire community vulnerable to the infection. We also analyze the performance of our model in the presence and absence of bots detected using an existing state-of-the-art bot detection model. Using topology- and activity-based trust properties sampled and aggregated from neighborhood of nodes, we are able to predict false information spreaders better than refutation information spreaders.

Supplementary information

The online version contains supplementary material available at 10.1007/s13278-022-00890-z.

SUBMITTER: Rath B

PROVIDER: S-EPMC9244065 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:IntroductionMisinformation surrounding COVID-19 poses a global public health problem that adversely affects governments' abilities to mitigate the disease and causes accidental deaths and self-harm due to false beliefs about the virus, prevention measures, vaccines and cures. We aim to examine the relationship between exposure to and trust in COVID-19 news (from Television, social media, interpersonal communication) and information sources (healthcare experts, government, clerics) and belief in COVID-19 myths and false information, as well as critical verification practices before posting on social media.MethodsWe use a cross-sectional researcher-administered phone survey of adults living in Lebanon between March 27 and April 23, 2020.ResultsThe sample included 56.1% men and 43.9% women, 37.9% with a university degree, 63.0% older than 30, and 7% with media literacy training. Those who trust COVID-19 news from social media [95%CI:(1.05-1.52)] and interpersonal communication [95%CI:(1.25-1.82)], and those who trust information from clerics [95%CI:(1.25-1.82)] were more likely to believe in COVID-19 myths and false information. University graduates [95%CI:(0.25-0.51)] and those who trust information from government [95%CI:(0.65-0.89] were less likely to believe in myths and false information. Those who believe in COVID-19 myths and false information [95%CI:(0.25-0.70)] were less likely to engage in critical social media posting practices. Only those who underwent media literacy training [95%CI:(1.24-6.55)] were more likely to engage in critical social media posting practices.ConclusionHigher education and trust in information from government contributed to decreasing belief in COVID-19 myths and false information. Trust in news from social media, interpersonal communication and clerics contributed to increasing belief in COVID-19 myths and false information, which in turn contributed to less critical social media posting practices, thereby exacerbated the infodemic. Media literacy training contributed to increasing critical social media posting practices, thereby played a role in mitigating the infodemic.

Dataset Information

Fake news spreader detection using trust-based strategies in social networks with bot filtration.

Supplementary information

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