Journal of Engineering Research
Abstract
The increased use of social media platforms has made it easier to publish and distribute news items, but it has also opened up new opportunities for distributing fake news. Fake news is information that has been written with the goal of misleading or deceiving readers. As a result, there is a need for efficient false news identification tools where the information can be gathered from the text of posts or from publicly available social data (such as user information or feedback on articles or the social network). The detection of fake news in its early stages is a major challenge. In this paper, PFND-HF (parallel fake news detection through hybrid features) was proposed. PFND-HF is a hybrid-feature-based classification algorithm designed to detect fake news early. It depended on the tweet text, the user profiles, and the replies. PFND-HF works in a parallel way in order to reduce the processing time. This parallel way reduces the processing time by 30% in comparison with not using it. PFND-HF contains the proposed user model that was critical in identifying whether the tweet was fake or not.
Recommended Citation
Elsaieed, asmaa mohemed DR
(2024)
"A Parallel Methodology for Early Fake News Detection Based on Hybrid Features on Social Media,"
Journal of Engineering Research: Vol. 8:
Iss.
4, Article 14.
Available at:
https://digitalcommons.aaru.edu.jo/erjeng/vol8/iss4/14