Identification and Analysis of the Spread of {Mis}information on Social Media

Document Type

Conference Proceeding

Publication Date

2-2024

Publication Title

Computational Data and Social Networks (CSoNet 2023)

Pages

361-372

Publisher Name

Springer

Abstract

With unfolding crises such as the COVID-19 pandemic, it is essential that factual information is dispersed at a rapid pace. One of the major setbacks to mitigating the effects of such crises is misinformation. Advancing technologies such as transformer-based architectures that can pick up underlying patterns and correlational information that constitutes information provide tools that can be used to identify what is misinformation/information. To identify and analyze the spread of misinformation, this work performs a quantitative analysis that uses X (previously Twitter) as the data source and a BERT-based model to identify misinformation. The information of the posts, users, and followers was collected based on hashtags and then processed and manually labeled. Furthermore, we tracked the spread of misinformation related to COVID-19 during the year 2021 and determined how communities that spread information and/or misinformation on social networks interact from an analytical perspective. Our findings suggest that users tend to post more misinformation than information, possibly intentionally spreading misinformation. Our model showed good performance in classifying tweets as information/misinformation, resulting in an accuracy of 86%.

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Author Posting © The Authors, 2024.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

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