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AJAO, Oluwaseun, GARG, Ashish and DA COSTA ABREU, Marjory (2022). Exploring content-based and meta-data analysis for detecting fake news infodemic: a case study on COVID-19. In: 2022 12th International Conference of Pattern Recognition Systems (ICPRS 2022). IEEE.
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ICPRS2022_Seun-paper.pdf - Accepted Version All rights reserved. Download (602kB) | Preview |
Abstract
The coronavirus pandemic (COVID-19) is probably the most disruptive global health disaster in recent history. It negatively impacted the whole world and virtually brought the global economy to a standstill. However, as the virus was spreading, infecting people and claiming thousands of lives so was the spread and propagation of fake news, misinformation and disinformation about the event. These included the spread of unconfirmed health advice and remedies on social media. In this paper, false information about the pandemic is identified using a content-based approach and metadata curated from messages posted to online social networks. A content-based approach combined with metadata as well as an initial feature analysis is used and then several supervised learning models are tested for identifying and predicting misleading posts. Our approach shows up to 93 % accuracy in the detection of fake news related posts about the COVID-19 pandemic.
| Item Type: | Book Section |
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| Additional Information: | 2022 12th International Conference on Pattern Recognition Systems (ICPRS), 7 June 2022-10 June 2022, Saint-Etienne, France. © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Identification Number: | https://doi.org/10.1109/ICPRS54038.2022.9854058 |
| SWORD Depositor: | Symplectic Elements |
| Depositing User: | Symplectic Elements |
| Date Deposited: | 29 Mar 2022 12:06 |
| Last Modified: | 12 Oct 2023 10:30 |
| URI: | https://shura.shu.ac.uk/id/eprint/29955 |
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