A graph mining-based approach to analyze the dynamics of the Twitter community of COVID-19 misinformation disseminators
This study explores the global problem of misinformation dissemination on social media, particularly Twitter, due to the COVID-19 pandemic. It identifies prominent disseminators, investigates the spread of false information and the ecosystem of disinformation spreaders, and assesses their online per...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | ICT Express |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405959524001358 |
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| author | Asma Ul Hussna Risul Islam Md Golam Rabiul Alam Jia Uddin Imran Ashraf Md Abdus Samad |
| author_facet | Asma Ul Hussna Risul Islam Md Golam Rabiul Alam Jia Uddin Imran Ashraf Md Abdus Samad |
| author_sort | Asma Ul Hussna |
| collection | DOAJ |
| description | This study explores the global problem of misinformation dissemination on social media, particularly Twitter, due to the COVID-19 pandemic. It identifies prominent disseminators, investigates the spread of false information and the ecosystem of disinformation spreaders, and assesses their online personalities. We track the interaction among fake news spreaders using the User–User Interaction Graph. The study reveals a rapidly growing population of disseminators, including professional spreaders, with over 3% dominating the others. The collaboration among fake news spreaders is high, highlighting the need for further research using publicly available online data to understand the community spreading malicious misinformation about COVID-19. |
| format | Article |
| id | doaj-art-d0f115c841cf4b6db6c635da1bf0b7d6 |
| institution | OA Journals |
| issn | 2405-9595 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | ICT Express |
| spelling | doaj-art-d0f115c841cf4b6db6c635da1bf0b7d62025-08-20T02:21:46ZengElsevierICT Express2405-95952024-12-011061280128710.1016/j.icte.2024.10.006A graph mining-based approach to analyze the dynamics of the Twitter community of COVID-19 misinformation disseminatorsAsma Ul Hussna0Risul Islam1Md Golam Rabiul Alam2Jia Uddin3Imran Ashraf4Md Abdus Samad5Department of Computer Science and Engineering, BRAC University, Dhaka, BangladeshPalo Alto Networks Inc., Santa Clara, CA, USADepartment of Computer Science and Engineering, BRAC University, Dhaka, BangladeshAI and Big Data Department, Endicott College, Woosong University, Daejeon, Republic of KoreaDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan-si, Republic of Korea; Corresponding authors.Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si, Republic of Korea; Corresponding authors.This study explores the global problem of misinformation dissemination on social media, particularly Twitter, due to the COVID-19 pandemic. It identifies prominent disseminators, investigates the spread of false information and the ecosystem of disinformation spreaders, and assesses their online personalities. We track the interaction among fake news spreaders using the User–User Interaction Graph. The study reveals a rapidly growing population of disseminators, including professional spreaders, with over 3% dominating the others. The collaboration among fake news spreaders is high, highlighting the need for further research using publicly available online data to understand the community spreading malicious misinformation about COVID-19.http://www.sciencedirect.com/science/article/pii/S2405959524001358COVID-19Fake newsCommunity analysisTwitterDisseminatorMisinformation |
| spellingShingle | Asma Ul Hussna Risul Islam Md Golam Rabiul Alam Jia Uddin Imran Ashraf Md Abdus Samad A graph mining-based approach to analyze the dynamics of the Twitter community of COVID-19 misinformation disseminators ICT Express COVID-19 Fake news Community analysis Disseminator Misinformation |
| title | 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_short | 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 |
| topic | COVID-19 Fake news Community analysis Disseminator Misinformation |
| url | http://www.sciencedirect.com/science/article/pii/S2405959524001358 |
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