Detecting COVID-19-Related Fake News Using Feature Extraction

Since its emergence in December 2019, there have been numerous posts and news regarding the COVID-19 pandemic in social media, traditional print, and electronic media. These sources have information from both trusted and non-trusted medical sources. Furthermore, the news from these media are spread rapidly. Spreading a piece of deceptive information may lead to anxiety, unwanted exposure to medical remedies, tricks for digital marketing, and may lead to deadly factors. Therefore, a model for detecting fake news from the news pool is essential. In this work, the dataset which is a fusion of news related to COVID-19 that has been sourced from data from several social media and news sources is used for classification. In the first step, preprocessing is performed on the dataset to remove unwanted text, then tokenization is carried out to extract the tokens from the raw text data collected from various sources. Later, feature selection is performed to avoid the computational overhead incurred in processing all the features in the dataset. The linguistic and sentiment features are extracted for further processing. Finally, several state-of-the-art machine learning algorithms are trained to classify the COVID-19-related dataset. These algorithms are then evaluated using various metrics. The results show that the random forest classifier outperforms the other classifiers with an accuracy of 88.50%.

Automated summary

Since December 2019, there has been an abundance of posts and news about the COVID-19 pandemic on social media, traditional print, and electronic media, which may lead to anxiety and unnecessary exposure to medical remedies. In response to this issue, the author used a dataset fused from multiple sources and trained several machine learning algorithms for classifying COVID-19 related news after preprocessing, tokenization, and feature selection steps. The results showed that the random forest classifier performed the best with an accuracy of 88.50%.