Hamed Suhaib Kh, Ab Aziz Mohd Juzaiddin, Yaakub Mohd Ridzwan
Center for Software Technology and Management (SOFTAM), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, Malaysia.
Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, Malaysia.
Heliyon. 2023 Sep 21;9(10):e20382. doi: 10.1016/j.heliyon.2023.e20382. eCollection 2023 Oct.
Currently, social networks have become the main source to acquire news about current global affairs. However, fake news appears and spreads on social media daily. This disinformation has a negative influence on several domains, such as politics, the economy, and health. In addition, it further generates detriments to societal stability. Several studies have provided effective models for detecting fake news in social networks through a variety of methods; however, there are limitations. Furthermore, since it is a critical field, the accuracy of the detection models was found to be notably insufficient. Although many review articles have addressed the repercussions of fake news, most have focused on specific and recurring aspects of fake news detection models. For example, the majority of reviews have primarily focused on dividing datasets, features, and classifiers used in this field by type. The limitations of the datasets, their features, how these features are fused, and the impact of all these factors on detection models were not investigated, especially since most detection models were based on a supervised learning approach. This review article analyzes relevant studies for the few last years and highlights the challenges faced by fake news detection models and their impact on their performance. The investigation of fake news detection studies relied on the following aspects and their impact on detection accuracy, namely datasets, overfitting/underfitting, image-based features, feature vector representation, machine learning models, and data fusion. Based on the analysis of relevant studies, the review showed that these issues significantly affect the performance and accuracy of detection models. This review aims to provide room for other researchers in the future to improve fake news detection models.
目前，社交网络已成为获取全球时事新闻的主要来源。然而，假新闻每天都在社交媒体上出现并传播。这种虚假信息在政治、经济和健康等多个领域都有负面影响。此外，它还会对社会稳定造成进一步损害。一些研究通过多种方法为社交网络中的假新闻检测提供了有效的模型；然而，这些模型存在局限性。此外，由于这是一个关键领域，发现检测模型的准确性明显不足。尽管许多综述文章都讨论了假新闻的影响，但大多数都集中在假新闻检测模型的特定和反复出现的方面。例如，大多数综述主要集中在按类型划分该领域中使用的数据集、特征和分类器。没有研究数据集的局限性、它们的特征、这些特征如何融合以及所有这些因素对检测模型的影响，特别是因为大多数检测模型基于监督学习方法。这篇综述文章分析了过去几年的相关研究，突出了假新闻检测模型面临的挑战及其对性能的影响。对假新闻检测研究的调查依赖于以下几个方面及其对检测准确性的影响，即数据集、过拟合/欠拟合、基于图像的特征、特征向量表示、机器学习模型和数据融合。基于对相关研究的分析，综述表明这些问题显著影响检测模型的性能和准确性。这篇综述旨在为未来其他研究人员改进假新闻检测模型提供空间。
