MAPX: An explainable model-agnostic framework for the detection of false information on social media networks

Sarah Condran, Michael Bewong, Selasi Kwashie, Md Zahidul Islam, Irfan Altas, Joshua Condran
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Abstract

The automated detection of false information has become a fundamental task in combating the spread of "fake news" on online social media networks (OSMN) as it reduces the need for manual discernment by individuals. In the literature, leveraging various content or context features of OSMN documents have been found useful. However, most of the existing detection models often utilise these features in isolation without regard to the temporal and dynamic changes oft-seen in reality, thus, limiting the robustness of the models. Furthermore, there has been little to no consideration of the impact of the quality of documents' features on the trustworthiness of the final prediction. In this paper, we introduce a novel model-agnostic framework, called MAPX, which allows evidence based aggregation of predictions from existing models in an explainable manner. Indeed, the developed aggregation method is adaptive, dynamic and considers the quality of OSMN document features. Further, we perform extensive experiments on benchmarked fake news datasets to demonstrate the effectiveness of MAPX using various real-world data quality scenarios. Our empirical results show that the proposed framework consistently outperforms all state-of-the-art models evaluated. For reproducibility, a demo of MAPX is available at \href{https://github.com/SCondran/MAPX_framework}{this link}
MAPX:用于检测社交媒体网络虚假信息的可解释模型无关框架
自动检测虚假信息已成为应对在线社交媒体网络(OSMN)上 "假新闻 "传播的一项基本任务,因为它减少了个人手动辨别的需要。在文献中,人们发现利用 OSMN 文档的各种内容或上下文特征非常有用。然而,现有的大多数检测模型往往孤立地利用这些特征,而不考虑现实中的时间和动态变化,从而限制了模型的鲁棒性。此外,几乎没有人考虑过文档特征的质量对最终预测可信度的影响。在本文中,我们介绍了一种名为 MAPX 的新型模型无关框架,它允许以可解释的方式基于证据聚合现有模型的预测结果。事实上,所开发的聚合方法是自适应的、动态的,并考虑了 OSMN 文档特征的质量。此外,我们还在基准假新闻数据集上进行了大量实验,利用各种真实世界的数据质量场景来证明 MAPX 的有效性。实证结果表明,所提出的框架始终优于所有接受评估的最先进模型。为便于重现,MAPX 的演示可在(href{https://github.com/SCondran/MAPX_framework}{this link} )上下载。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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