Misreporting and Fake News Detection Techniques on the Social Media Platform

作者：Yuran Feng · 发表于：Highlights in Science Engineering and Technology · 年份：2022 · DOI：10.54097/hset.v12i.1417 · 被引用次数：6 · 研究领域：Misinformation and Its Impacts、Spam and Phishing Detection、Advanced Malware Detection Techniques

One of the major concerns nowadays is the rapid spreading of fake news or unverified information on all kinds of social media. Misinformation and disinformation on the digital media of news distribution have brought significant negative impacts to our community, which the traditional techniques can no longer detect and deal with it effectively. It is urgent to squelch fake news immediately to limit its impact on the economy and society. As deep learning techniques continue developing in recent decades, scholars successfully deployed deep neural networks on fake news detection tasks. The first noticeable thing is to admit that the fake news detection task has made significant accomplishments as fast as we hoped. It is necessary to study further and broadly review the state-of-the-art fake news detection approaches. In this review paper, we survey several distinct deep learning techniques and provide a comprehensive review of automatic fake news detection classification tasks and the datasets and models used, demonstrating the performance evaluation on different approaches. We have also analyzed the potential challenge we encountered in fake news detection.