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(Mis-)leading the Covid-19 vaccination discourse on Twitter— A study of infodemic around the pandemic
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Research Article on arxiv
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Additional Info
| Field | Value |
|---|---|
| Detailed description | The ongoing discourse in social media has amplified the fears, uncertainties, and doubts (FUD) surrounding COVID-19 and the currently available vaccines, leading to an infodemic (a portmanteau of information and epidemic, referring to the spread of potentially accurate and inaccurate information about a disease spreading like an epidemic). In this work, we try to identify false information on social media in order to battle misconceptions about COVID-19. Specifically, we collected a corpus of COVID-19 vaccination-related tweets (over 200,000) over the course of seven months (September 2020 - March 2021). First we extracted various features from text and then we exploit these features for classification task of predicting between misleading and non-misleading tweets. Specifically, we applied various machine learning models with up to 90% accuracy are used for prediction, and the value of each feature is explained using the SHAP Explainable AI (XAI) tool. |
| Group | Societal Debates and Misinformation |
| Involved People | Sharma, Rajesh, rajesh.sharma@ut.ee, orcid.org/0000-0003-3581-1332 |
| State | Complete |
| Thematic Cluster | Social Data [SD] |
| Thematic Cluster | Social Network Analysis [SNA] |
| Thematic Cluster | Text and Social Media Mining [TSMM] |
| Thematic Cluster | Web Analytics [WA] |
| system:type | Experiment |
Management Info
| Field | Value |
|---|---|
| Author | Sharma Rajesh |
| Maintainer | Sharma Rajesh |
| Version | 1 |
| Last Updated | 7 September 2023, 14:32 (CEST) |
| Created | 9 October 2022, 18:44 (CEST) |