A Hybrid Multitask Learning Framework with a Fire Hawk Optimizer for Arabic Fake News Detection

The exponential spread of news and posts related to the COVID-19 pandemic on social media platforms led to the emergence of the disinformation phenomenon. The phenomenon of spreading fake information and news creates significant concern for the public health and safety of the population. In this paper, we propose a disinformation detection framework based on multi-task learning (MTL) and meta-heuristic algorithms in the context of the COVID-19 pandemic. The developed framework uses an MTL and a pre-trained transformer-based model to learn and extract contextual feature representations from Arabic social media posts. The extracted contextual representations are fed to an alternative feature selection technique which depends on modified version of the Fire Hawk Optimizer. The proposed framework, which aims to improve the disinformation detection rate, was evaluated on several datasets of Arabic social media posts. The experimental results show that the proposed framework can achieve accuracy of 59%. It obtained, at best, precision, recall, and F-measure of 53%, 71%, and 53%, respectively, on all datasets; and it outperformed the other algorithms in all measures.

Automated summary

The rapid spread of fake information and news related to the COVID-19 pandemic on social media platforms has raised serious concerns for public health and safety. This paper proposes a disinformation detection framework using multi-task learning and meta-heuristic algorithms to analyze Arabic social media posts. The experimental results show that the proposed framework achieves an accuracy of 59% and outperforms other algorithms in all evaluation measures.