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• W3204381888 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." @default.
• W3204381888 created "2021-10-11" @default.
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• W3204381888 date "2021-09-23" @default.
• W3204381888 modified "2023-10-04" @default.
• W3204381888 title "Machine Learning in Detecting COVID-19 Misinformation on Twitter" @default.
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• W3204381888 doi "https://doi.org/10.3390/fi13100244" @default.
• W3204381888 hasPublicationYear "2021" @default.
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