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Rumor Detection on Social Media with Event Augmentations

Published: 11 July 2021 Publication History

Abstract

With the rapid growth of digital data on the Internet, rumor detection on social media has been vital. Existing deep learning-based methods have achieved promising results due to their ability to learn high-level representations of rumors. Despite the success, we argue that these approaches require large reliable labeled data to train, which is time-consuming and data-inefficient. To address this challenge, we present a new solution, Rumor Detection on social media with Event Augmentations (RDEA), which innovatively integrates three augmentation strategies by modifying both reply attributes and event structure to extract meaningful rumor propagation patterns and to learn intrinsic representations of user engagement. Moreover, we introduce contrastive self-supervised learning for the efficient implementation of event augmentations and alleviate limited data issues. Extensive experiments conducted on two public datasets demonstrate that RDEA achieves state-of-the-art performance over existing baselines. Besides, we empirically show the robustness of RDEA when labeled data are limited.

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  • (2025)Do not wait: Preemptive rumor detection with cooperative LLMs and accessible social contextInformation Processing & Management10.1016/j.ipm.2024.10399562:3(103995)Online publication date: May-2025
  • (2025)Early rumor detection method based on stage sampling and triple-relationship graphThe Journal of Supercomputing10.1007/s11227-025-06959-881:3Online publication date: 10-Feb-2025
  • (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
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cover image ACM Conferences
SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 2021
2998 pages
ISBN:9781450380379
DOI:10.1145/3404835
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 11 July 2021

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Author Tags

  1. contrastive learning
  2. event augmentation
  3. rumor detection

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

View all
  • (2025)Do not wait: Preemptive rumor detection with cooperative LLMs and accessible social contextInformation Processing & Management10.1016/j.ipm.2024.10399562:3(103995)Online publication date: May-2025
  • (2025)Early rumor detection method based on stage sampling and triple-relationship graphThe Journal of Supercomputing10.1007/s11227-025-06959-881:3Online publication date: 10-Feb-2025
  • (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)Contrastive Learning with Edge‐Wise Augmentation for Rumor DetectionInternational Journal of Intelligent Systems10.1155/2024/38585262024:1Online publication date: 9-Aug-2024
  • (2024)Detecting Misinformation on Social Media using Community Insights and Contrastive LearningACM Transactions on Intelligent Systems and Technology10.1145/370900916:2(1-27)Online publication date: 19-Dec-2024
  • (2024)Bayesian Graph Local Extrema Convolution with Long-tail Strategy for Misinformation DetectionACM Transactions on Knowledge Discovery from Data10.1145/363940818:4(1-21)Online publication date: 12-Feb-2024
  • (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
  • (2024)MKV: Mapping Key Semantics into Vectors for Rumor DetectionProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657937(2512-2516)Online publication date: 10-Jul-2024
  • (2024)Predicting Micro-video Popularity via Multi-modal Retrieval AugmentationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657929(2579-2583)Online publication date: 10-Jul-2024
  • (2024)Propagation Structure Fusion for Rumor Detection Based on Node-Level Contrastive LearningIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.331966135:12(18649-18660)Online publication date: Dec-2024
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