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Divide-and-Conquer: Post-User Interaction Network for Fake News Detection on Social Media

Published: 25 April 2022 Publication History

Abstract

Fake News detection has attracted much attention in recent years. Social context based detection methods attempt to model the spreading patterns of fake news by utilizing the collective wisdom from users on social media. This task is challenging for three reasons: (1) There are multiple types of entities and relations in social context, requiring methods to effectively model the heterogeneity. (2) The emergence of news in novel topics in social media causes distribution shifts, which can significantly degrade the performance of fake news detectors. (3) Existing fake news datasets usually lack of great scale, topic diversity and user social relations, impeding the development of this field. To solve these problems, we formulate social context based fake news detection as a heterogeneous graph classification problem, and propose a fake news detection model named Post-User Interaction Network (PSIN), which adopts a divide-and-conquer strategy to model the post-post, user-user and post-user interactions in social context effectively while maintaining their intrinsic characteristics. Moreover,we adopt an adversarial topic discriminator for topic-agnostic feature learning, in order to improve the generalizability of our method for new-emerging topics. Furthermore, we curate a new dataset for fake news detection, which contains over 27,155 news from 5 topics, 5 million posts, 2 million users and their induced social graph with 0.2 billion edges. It has been published on https://github.com/qwerfdsaplking/MC-Fake. Extensive experiments illustrate that our method outperforms SOTA baselines in both in-topic and out-of-topic settings.

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        cover image ACM Conferences
        WWW '22: Proceedings of the ACM Web Conference 2022
        April 2022
        3764 pages
        ISBN:9781450390965
        DOI:10.1145/3485447
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        Published: 25 April 2022

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        Author Tags

        1. Fake News Detection
        2. Graph Neural Network
        3. Social Media

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        • The University of Manchester - China Scholarship Council joint scholarship award

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        WWW '22: The ACM Web Conference 2022
        April 25 - 29, 2022
        Virtual Event, Lyon, France

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        Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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        • (2024)SAMGAT: structure-aware multilevel graph attention networks for automatic rumor detectionPeerJ Computer Science10.7717/peerj-cs.220010(e2200)Online publication date: 18-Jul-2024
        • (2024)Fake News Detection Revisited: An Extensive Review of Theoretical Frameworks, Dataset Assessments, Model Constraints, and Forward-Looking Research AgendasTechnologies10.3390/technologies1211022212:11(222)Online publication date: 6-Nov-2024
        • (2024)A Predictive Model for Benchmarking the Performance of Algorithms for Fake and Counterfeit News Classification in Global NetworksSensors10.3390/s2417581724:17(5817)Online publication date: 7-Sep-2024
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        • (2024)Propagation Structure-Aware Graph Transformer for Robust and Interpretable Fake News DetectionProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672024(4652-4663)Online publication date: 25-Aug-2024
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