Enhancing Fake News Detection Using a Multimodal Approach by Analyzing Texts, Images, and Video

Enhancing Fake News Detection Using a Multimodal Approach by Analyzing Texts, Images, and Videos

© 2023 by IJETT Journal
Volume-71 Issue-10
Year of Publication : 2023
Author : Majed Alsafyani
DOI : 10.14445/22315381/IJETT-V71I10P230

How to Cite?

Majed Alsafyani, "Enhancing Fake News Detection Using a Multimodal Approach by Analyzing Texts, Images, and Videos," International Journal of Engineering Trends and Technology, vol. 71, no. 10, pp. 336-345, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I10P230

The proliferation of fake news in our contemporary society has emerged as a pressing and concerning issue. The widespread use of social media has facilitated the effortless dissemination of false information, making it increasingly challenging to discern truth from fiction. In this article, we propose a novel deep learning-based approach designed to classify and detect fake news in the Arabic language, with a particular focus on social media platforms, specifically Twitter. Leveraging text, image, and video data, our method demonstrates the potential to identify and flag fake news effectively. We evaluate the performance of our approach using a dataset comprising Arabic tweets and report promising results. The achieved high accuracy in detecting fake news on Twitter underscores the efficacy of our method in tackling the pervasive problem of fake news in the Arabic language. We obtained an accuracy of 92%. This research significantly contributes to combating the spread of misinformation and upholds the importance of effective solutions in addressing this critical societal concern.

KeywordsFake news, Text, Image, Video, Deep learning.

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