Machine Learning in Detecting COVID-19 Misinformation on Twitter
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 oft...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Future Internet |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-5903/13/10/244 |
| _version_ | 1827679469296418816 |
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| author | Mohammed N. Alenezi Zainab M. Alqenaei |
| author_facet | Mohammed N. Alenezi Zainab M. Alqenaei |
| author_sort | Mohammed N. Alenezi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T06:32:51Z |
| format | Article |
| id | doaj.art-85109a8c951849c291f89f17356b860a |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-10T06:32:51Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-85109a8c951849c291f89f17356b860a2023-11-22T18:19:36ZengMDPI AGFuture Internet1999-59032021-09-01131024410.3390/fi13100244Machine Learning in Detecting COVID-19 Misinformation on TwitterMohammed N. Alenezi0Zainab M. Alqenaei1Computer Science and Information Systems Department, The Public Authority for Applied Education and Training, Safat 13147, KuwaitInformation Systems and Operations Management Department, Kuwait University, Safat 13055, KuwaitSocial 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.https://www.mdpi.com/1999-5903/13/10/244misinformationLSTMMC-CNNKNNTwitterCOVID-19 |
| spellingShingle | Mohammed N. Alenezi Zainab M. Alqenaei Machine Learning in Detecting COVID-19 Misinformation on Twitter Future Internet misinformation LSTM MC-CNN KNN COVID-19 |
| title | Machine Learning in Detecting COVID-19 Misinformation on Twitter |
| title_full | Machine Learning in Detecting COVID-19 Misinformation on Twitter |
| title_fullStr | Machine Learning in Detecting COVID-19 Misinformation on Twitter |
| title_full_unstemmed | Machine Learning in Detecting COVID-19 Misinformation on Twitter |
| title_short | Machine Learning in Detecting COVID-19 Misinformation on Twitter |
| title_sort | machine learning in detecting covid 19 misinformation on twitter |
| topic | misinformation LSTM MC-CNN KNN COVID-19 |
| url | https://www.mdpi.com/1999-5903/13/10/244 |
| work_keys_str_mv | AT mohammednalenezi machinelearningindetectingcovid19misinformationontwitter AT zainabmalqenaei machinelearningindetectingcovid19misinformationontwitter |