MAPX：一种用于社交媒体网络虚假信息检测的可解释模型无关框架
MAPX: An explainable model-agnostic framework for the detection of false information on social media networks
AI总结：
针对现有虚假信息检测模型忽略特征时序动态变化、未考虑特征质量对预测可信度影响的问题，本文提出可解释的模型无关框架MAPX，通过自适应动态聚合多模型预测提升检测效果，在基准数据集上性能优于现有SOTA模型。
摘要：
虚假信息的自动检测已成为打击在线社交媒体网络（OSMN）上“假新闻”传播的一项基础任务，因为它减少了个人人工辨别的需求。现有研究发现，利用OSMN文档的各类内容或上下文特征是有效的。然而，大多数现有检测模型往往孤立地使用这些特征，未考虑现实中常见的时序和动态变化，从而限制了模型的鲁棒性。此外，现有研究几乎未考虑文档特征质量对最终预测可信度的影响。本文提出了一种名为MAPX的新型模型无关框架，该框架能够以可解释的方式对现有模型的预测结果进行基于证据的聚合。实际上，所开发的聚合方法具有自适应性、动态性，并且会考虑OSMN文档特征的质量。此外，我们在基准假新闻数据集上开展了大量实验，在多种真实世界数据质量场景下验证了MAPX的有效性。实验结果表明，所提出的框架性能始终优于所有参与评估的SOTA模型。为保证可复现性，MAPX的演示版本可通过此链接获取：https://github.com/SCondran/MAPX_framework
英文摘要：
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}
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
