Machine Learning in Detecting COVID-19 Misinformation on Twitter

Mohammed N. Alenezi and Zainab M. Alqenaei
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Mohammed N. Alenezi: Computer Science and Information Systems Department, The Public Authority for Applied Education and Training, Safat 13147, Kuwait
Zainab M. Alqenaei: Information Systems and Operations Management Department, Kuwait University, Safat 13055, Kuwait

Future Internet, 2021, vol. 13, issue 10, 1-20

Abstract: Social media platforms such as Facebook, Instagram, and Twitter are an inevitable part of our daily lives. These social media platforms are effective tools for disseminating news, photos, and other types of information. In addition to the positives of the convenience of these platforms, they are often used for propagating malicious data or information. This misinformation may misguide users and even have dangerous impact on society’s culture, economics, and healthcare. The propagation of this enormous amount of misinformation is difficult to counter. Hence, the spread of misinformation related to the COVID-19 pandemic, and its treatment and vaccination may lead to severe challenges for each country’s frontline workers. Therefore, it is essential to build an effective machine-learning (ML) misinformation-detection model for identifying the misinformation regarding COVID-19. In this paper, we propose three effective misinformation detection models. The proposed models are long short-term memory (LSTM) networks, which is a special type of RNN; a multichannel convolutional neural network (MC-CNN); and k-nearest neighbors (KNN). Simulations were conducted to evaluate the performance of the proposed models in terms of various evaluation metrics. The proposed models obtained superior results to those from the literature.

Keywords: misinformation; LSTM; MC-CNN; KNN; Twitter; COVID-19; machine learning (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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