A Novel Hybrid Deep Learning Model for Detecting COVID-19-Related Rumors on Social Media Based on LSTM and Concatenated Parallel CNNs

• Main Authors: Mohammed Al-Sarem, Abdullah Alsaeedi, Faisal Saeed, Wadii Boulila, Omair AmeerBakhsh
• Format: Article
• Language: English
• Published: MDPI AG 2021-08-01
• Series: Applied Sciences
• Subjects: rumor detection; deep learning; twitter analysis; convolution neural networks; LSTM; pretrained model
• Online Access: https://www.mdpi.com/2076-3417/11/17/7940

Spreading rumors in social media is considered under cybercrimes that affect people, societies, and governments. For instance, some criminals create rumors and send them on the internet, then other people help them to spread it. Spreading rumors can be an example of cyber abuse, where rumors or lies about the victim are posted on the internet to send threatening messages or to share the victim’s personal information. During pandemics, a large amount of rumors spreads on social media very fast, which have dramatic effects on people’s health. Detecting these rumors manually by the authorities is very difficult in these open platforms. Therefore, several researchers conducted studies on utilizing intelligent methods for detecting such rumors. The detection methods can be classified mainly into machine learning-based and deep learning-based methods. The deep learning methods have comparative advantages against machine learning ones as they do not require preprocessing and feature engineering processes and their performance showed superior enhancements in many fields. Therefore, this paper aims to propose a Novel Hybrid Deep Learning Model for Detecting COVID-19-related Rumors on Social Media (LSTM–PCNN). The proposed model is based on a Long Short-Term Memory (LSTM) and Concatenated Parallel Convolutional Neural Networks (PCNN). The experiments were conducted on an ArCOV-19 dataset that included 3157 tweets; 1480 of them were rumors (46.87%) and 1677 tweets were non-rumors (53.12%). The findings of the proposed model showed a superior performance compared to other methods in terms of accuracy, recall, precision, and F-score.