A graph mining-based approach to analyze the dynamics of the Twitter community of COVID-19 misinformation disseminators
This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science, 2024.
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Brac University
2024
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| Acesso em linha: | http://hdl.handle.net/10361/24135 |
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10361-241352024-09-25T06:35:44Z A graph mining-based approach to analyze the dynamics of the Twitter community of COVID-19 misinformation disseminators Hussna, Asma Ul Alam, Md. Golam Rabiul Department of Computer Science and Engineering, Brac University Dissemination COVID-19 Infodemic Misinformation Social network analysis Misinformation--Social media. Data mining. Social Media. This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science, 2024. Cataloged from PDF version of thesis. Includes bibliographical references (pages no.42-48). 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. Asma Ul Hussna M.Sc. in Computer Science 2024-09-19T05:21:13Z 2024-09-19T05:21:13Z ©2024 2024-04 Thesis ID 21166030 http://hdl.handle.net/10361/24135 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 62 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Dissemination COVID-19 Infodemic Misinformation Social network analysis Misinformation--Social media. Data mining. Social Media. |
| spellingShingle |
Dissemination COVID-19 Infodemic Misinformation Social network analysis Misinformation--Social media. Data mining. Social Media. Hussna, Asma Ul A graph mining-based approach to analyze the dynamics of the Twitter community of COVID-19 misinformation disseminators |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science, 2024. |
| author2 |
Alam, Md. Golam Rabiul |
| author_facet |
Alam, Md. Golam Rabiul Hussna, Asma Ul |
| format |
Thesis |
| author |
Hussna, Asma Ul |
| author_sort |
Hussna, Asma Ul |
| title |
A graph mining-based approach to analyze the dynamics of the Twitter community of COVID-19 misinformation disseminators |
| title_short |
A graph mining-based approach to analyze the dynamics of the Twitter community of COVID-19 misinformation disseminators |
| title_full |
A graph mining-based approach to analyze the dynamics of the Twitter community of COVID-19 misinformation disseminators |
| title_fullStr |
A graph mining-based approach to analyze the dynamics of the Twitter community of COVID-19 misinformation disseminators |
| title_full_unstemmed |
A graph mining-based approach to analyze the dynamics of the Twitter community of COVID-19 misinformation disseminators |
| title_sort |
graph mining-based approach to analyze the dynamics of the twitter community of covid-19 misinformation disseminators |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/24135 |
| work_keys_str_mv |
AT hussnaasmaul agraphminingbasedapproachtoanalyzethedynamicsofthetwittercommunityofcovid19misinformationdisseminators AT hussnaasmaul graphminingbasedapproachtoanalyzethedynamicsofthetwittercommunityofcovid19misinformationdisseminators |
| _version_ |
1814308276520615936 |