A graph mining-based approach to analyze the dynamics of the Twitter community of COVID-19 misinformation disseminators
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Date
2024-04Publisher
Brac UniversityAuthor
Hussna, Asma UlMetadata
Show full item recordAbstract
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.