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Generalized News Event Discovery via Dynamic Augmentation and Entropy Optimization

Published: 28 October 2024 Publication History

Abstract

News event discovery refers to the identification and detection of news events using multimodal data on social media. Currently, most works assume that the test set consists of known events. However, in real life, the emergence of new events is more frequent, which invalidates this assumption. In this paper, we propose a Dynamic Augmentation and Entropy Optimization (DAEO) model to address the scenario of generalized news event discovery, which requires the model to not only identify known events but also distinguish various new events. Specifically, we first introduce a multimodal augmentation module, which utilizes adversarial learning to enhance the multimodal representation capability. Secondly, we design an adaptive entropy optimization strategy combined with a self-distillation method, which uses multi-view pseudo-label consistency to improve the model's performance on both known and new events. In addition, we collect a multimodal news event discovery (MNED) dataset of 161,350 samples annotated with 66 real-world events. Extensive experimental results on the MNED dataset demonstrate the effectiveness of our proposed method. Our dataset is available on https://github.com/RetrainIt/MNED.

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      cover image ACM Conferences
      MM '24: Proceedings of the 32nd ACM International Conference on Multimedia
      October 2024
      11719 pages
      ISBN:9798400706868
      DOI:10.1145/3664647
      This work is licensed under a Creative Commons Attribution International 4.0 License.

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      Published: 28 October 2024

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      Author Tags

      1. generalized news event discovery
      2. social media

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      • The Guangdong Basic and Applied Basic Research Foundation
      • The Hong Kong Research Grants Council
      • The National Natural Science Foundation of China

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      MM '24: The 32nd ACM International Conference on Multimedia
      October 28 - November 1, 2024
      Melbourne VIC, Australia

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      MM '24 Paper Acceptance Rate 1,150 of 4,385 submissions, 26%;
      Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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