article · Mathematics
The rapid spread of online content surrounding the COVID-19 pandemic has intensified concerns regarding public health and safety due to digital disinformation. To tackle this challenge in Arabic content, a specialised detection framework combines multi-task learning with meta-heuristic algorithms. The architecture utilises a pre-trained transformer-based model alongside multi-task learning to extract contextual representations from Arabic social media posts. Following this feature extraction stage, a modified version of the Fire Hawk Optimizer performs feature selection to refine the data. Tested across several datasets of Arabic social media posts related to the pandemic, the system improved detection rates compared to baseline approaches. The framework achieved an accuracy of 59 percent, reaching a precision of 53 percent, a recall of 71 percent, and an F-measure of 53 percent, demonstrating superior performance over competing algorithms across all evaluated metrics.
Online disinformation during health crises poses serious risks to public safety. Automatically identifying misleading Arabic text is technically difficult due to linguistic complexities. By improving the detection rate of false information on social media platforms, this research addresses a major obstacle in automated content moderation, helping to limit the harmful societal effects of viral health-related falsehoods.
This tool could enable social media platforms or content-moderation organisations to screen Arabic health communications for false claims. Because the framework achieved an accuracy of 59 percent and an F-measure of 53 percent on experimental datasets, it remains at an early stage of research. Substantial further refinement and performance improvements would be required before the system is viable for operational, real-world deployment.
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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.
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DOI: 10.3390/math11020258
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