RPf-GCNs: reciprocal perspective driven fused GCNs for rumor detection on social media

IF 8.6 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Journal of Big Data Pub Date : 2024-01-09 DOI:10.1186/s40537-023-00866-6

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Abstract

The earliest detection of rumors across social media is the need to the hour in present global village. User’s are seamlessly connected in an unstructured network leading to rapid flow of information. User’s on the social media with malign intents may share defamatory content to contribute towards the fifth generation media warfare. The ingress of such defamatory content into society can result in panic, uncertainty and demoralization the peoples. Due to the huge amount of content over social platforms, the detection of malicious contents is hard. Earlier research while focuses on content profiling and flow of information, however, the reciprocal perspective of the source and following contents is missing. In this research, a novel Reciprocal Perspective fused Graph Convolutional Neural Network (RPf-GCN) is proposed. The proposed framework incorporates twin GCNs to encode both the bottom-up and top-down perspectives, enhancing the understanding of rumor propagation. Moreover convolutional operation is employed to fuse reciprocal perspective, providing a holistic view of the conversations. To validate the efficacy of the proposed framework, we conducted a series of experiments using real-world datasets, including PHEME and SemEval. Experimentation performed illustrates that the proposed framework outperformed over various baselines in two different evaluation metrics namely Macro F1 (for PHEME 0.736, for SemEval 0.461) and Accuracy (for PHEME 0.748, for SemEval 0.658).

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RPf-GCNs：用于社交媒体谣言检测的互惠视角驱动融合 GCNs

摘要在当今的地球村，尽早发现社交媒体上的谣言是当务之急。用户在非结构化网络中无缝连接，导致信息快速流动。社交媒体上怀有恶意的用户可能会分享诽谤性内容，从而引发第五代媒体战争。这些诽谤性内容一旦流入社会，就会造成恐慌、不确定性并打击人们的士气。由于社交平台上的内容数量巨大，恶意内容很难被发现。早期的研究侧重于内容剖析和信息流，但缺少从来源和关注内容的互惠角度进行分析。本研究提出了一种新颖的对等视角融合图卷积神经网络（RPf-GCN）。该框架结合了双 GCN 来编码自下而上和自上而下的视角，从而增强了对谣言传播的理解。此外，还采用了卷积运算来融合对等视角，从而提供对话的整体视图。为了验证所提框架的有效性，我们使用 PHEME 和 SemEval 等真实世界数据集进行了一系列实验。实验结果表明，所提出的框架在两个不同的评估指标（即 Macro F1（PHEME 为 0.736，SemEval 为 0.461）和 Accuracy（PHEME 为 0.748，SemEval 为 0.658）上都优于各种基线。

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来源期刊

Journal of Big Data Computer Science-Information Systems

CiteScore

17.80

自引率

3.70%

发文量

105

审稿时长

13 weeks

期刊介绍： The Journal of Big Data publishes high-quality, scholarly research papers, methodologies, and case studies covering a broad spectrum of topics, from big data analytics to data-intensive computing and all applications of big data research. It addresses challenges facing big data today and in the future, including data capture and storage, search, sharing, analytics, technologies, visualization, architectures, data mining, machine learning, cloud computing, distributed systems, and scalable storage. The journal serves as a seminal source of innovative material for academic researchers and practitioners alike.