The Veracity Problem: Detecting False Information and its Propagation on Online Social Media Networks

Sarah Condran
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Abstract

Detecting false information on social media is critical in mitigating its negative societal impacts. To reduce the propagation of false information, automated detection provide scalable, unbiased, and cost-effective methods. However, there are three potential research areas identified which once solved improve detection. First, current AI-based solutions often provide a uni-dimensional analysis on a complex, multi-dimensional issue, with solutions differing based on the features used. Furthermore, these methods do not account for the temporal and dynamic changes observed within the document's life cycle. Second, there has been little research on the detection of coordinated information campaigns and in understanding the intent of the actors and the campaign. Thirdly, there is a lack of consideration of cross-platform analysis, with existing datasets focusing on a single platform, such as X, and detection models designed for specific platform. This work aims to develop methods for effective detection of false information and its propagation. To this end, firstly we aim to propose the creation of an ensemble multi-faceted framework that leverages multiple aspects of false information. Secondly, we propose a method to identify actors and their intent when working in coordination to manipulate a narrative. Thirdly, we aim to analyse the impact of cross-platform interactions on the propagation of false information via the creation of a new dataset.
真实性问题:检测虚假信息及其在网络社交媒体上的传播
检测社交媒体上的虚假信息对于减轻其负面社会影响至关重要。为了减少虚假信息的传播,自动检测提供了可扩展、无偏见和具有成本效益的方法。然而,目前发现了三个潜在的研究领域,一旦解决了这些问题,就能提高检测能力。首先,当前基于人工智能的解决方案通常会对复杂的多维问题进行单维分析,根据所使用的特征,解决方案会有所不同。此外,这些方法没有考虑到在文档生命周期内观察到的时间和动态变化。第二,在检测协调信息活动以及理解参与者和活动意图方面的研究很少。第三,缺乏对跨平台分析的考虑,现有的数据集主要集中在 X 等单一平台上,检测模型也是针对特定平台设计的。这项工作旨在开发有效检测虚假信息及其传播的方法。为此,我们首先提出创建一个多元集合框架,以利用虚假信息的多个方面。其次,我们提出了一种方法来识别行动者以及他们在协同操纵叙事时的意图。第三,我们旨在通过创建一个新的数据集,分析跨平台互动对虚假信息传播的影响。
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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