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Research on Interpretable Fake News Detection Technology Based on Co-Attention Mechanism

Published: 15 December 2023 Publication History

Abstract

The wide dissemination of false news is increasingly threatening both individuals and society. Aiming at the problem of insufficient interpretability in false news detection task, the model TCIN based on collaborative attention mechanism is proposed. The model integrates the advantages of the existing dEFEND model and GCAN model. It captures more detailed semantic features through three attention layers (sentence-level cooperative attention layer, word-level cooperative attention layer and sentence-comment cooperative attention layer). The experimental results show that, compared with the model before combination, the accuracy of this model is 9.7% higher on the data set of Weibo-20 and on Twitter15 data set the accuracy is increased by 7.43%.

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      ICCVIT '23: Proceedings of the 2023 International Conference on Computer, Vision and Intelligent Technology
      August 2023
      378 pages
      ISBN:9798400708701
      DOI:10.1145/3627341
      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 the author(s) 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: 15 December 2023

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      Author Tags

      1. Fake news
      2. Interpretable
      3. Key words: Co-attention

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      • Research-article
      • Research
      • Refereed limited

      Funding Sources

      • National Social Science Foundation of China
      • Top-notch Talents of the Discipline

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      ICCVIT 2023

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      ICCVIT '23 Paper Acceptance Rate 54 of 142 submissions, 38%;
      Overall Acceptance Rate 54 of 142 submissions, 38%

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