Work

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

Year: 2023

Type: conference-paper

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%.

Source: International Conference on Computer, Vision and Intelligent Technology (ICCVIT)

DOI: 10.1145/3627341.3630379

Authors: Wen Jiang, Mingshu Zhang, Bin Wei, Kelan Ren, Facheng Yan, Xiong Zhang

Institution: Chinese People's Armed Police Force Engineering University

Language: English

Topic: Misinformation and Its Impacts

Subfield: Sociology and Political Science

Field: Social Sciences

Domain: Social Sciences

Open Access status: bronze

Funder: National Social Science Fund of China

Award: G4805521509