A graph mining-based approach to analyze the dynamics of the Twitter community of COVID-19 misinformation disseminators

Abstract

The abundant dissemination of misinformation on social networks has emerged as a worldwide threat, exerting an implicit influence on public opinion and endangering the progress of social, political, and public health domains in general. Amidst the rapid worldwide dissemination of the COVID-19 virus, unfortunately, misinformation about COVID-19 is being created and disseminated at a startling rate. The dissemination of misleading information has led to vast disorientation, social disruptions, and severe repercussions for health-related issues. Moreover, the dissemination of fake or misleading information via social media networking, particularly Twitter, during the COVID-19 pandemic has resulted in an extensive proliferation of information, commonly referred to as an “infodemic.” In order to combat the dissemination of fake news, we have proposed a research model that can predict fake news related to the COVID-19 issue on social media data using classical classification methods such as multinomial na¨ıve bayes classifiers, logistic regression classifiers, and support vector machine classifiers. In addition, we have applied a deep learning-based algorithm named DistilBERT to accurately predict fake COVID-19 news. These approaches have been used in this paper to compare which technique is much more convenient for accurately predicting fake news about COVID-19 on social media posts. The objective of this study is to understand how information is deviating and misinformation is spreading through social media during the COVID- 19 pandemic. Also, this research aims to examine the ecosystem of individuals who spread misinformation, with the objectives of comprehending their collective actions, identifying the most influential disseminators, and examining their online personas and profiles. We leverage the UUIG (User-User Interaction Graph) to capture the misinformation disseminators’ behavioral interactions. The following research analysis reveals the following significant findings: (a) the population of disseminators is growing rapidly even though today; (b) the community of disseminators comprises professional spreaders; above 3% of the fake news spreading population dominates others; and (c) they exhibit a high degree of collaboration among the fake news spreaders; we observe five big communities of collaborators. Our work represents a notable advancement in utilizing publicly available online data to gain insights into the community that spreads malicious misinformation about COVID-19.