Detection of false information in environment-based complaint reports based on graph convolution

Qingwu Fan; Xiaoning Sun

doi:10.1117/12.2673482

3 April 2023 Detection of false information in environment-based complaint reports based on graph convolution

Qingwu Fan, Xiaoning Sun

Proceedings Volume 12599, Second International Conference on Digital Society and Intelligent Systems (DSInS 2022); 1259909 (2023) https://doi.org/10.1117/12.2673482
Event: 2nd International Conference on Digital Society and Intelligent Systems (DSInS 2022), 2022, Chendgu, China

Abstract

To solve the problem of strong subjectivity and low credibility of complaints, a new detection model of false information in environment complaints based on graph convolution was proposed. Firstly, the text of environment complaint is preprocessed: then the word in the complaint text and lexicon are used as nodes, the edges between words are constructed by point mutual information, the edges between text and words are constructed by term frequency-inverse document frequency, and the edges between text and text are constructed by text similarity. The edges between words, text-word relationship and text-text relationship are used to construct the text graph of environmental complaint based on word co-occurrence, text-word relationship and text-text relationship; finally, the node information is passed between nodes through graph convolution, and the coupling between complaint information is used to detect complaint false information. Through designing experiments, the model is compared with the methods of common classification models, and the experimental results show that the model has good performance. It is suitable to be applied in the task of environment-based false complaint detection.

Citation Download Citation

Qingwu Fan and Xiaoning Sun "Detection of false information in environment-based complaint reports based on graph convolution", Proc. SPIE 12599, Second International Conference on Digital Society and Intelligent Systems (DSInS 2022), 1259909 (3 April 2023); https://doi.org/10.1117/12.2673482

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KEYWORDS

Environmental sensing

Convolution

Data modeling

Education and training

Machine learning

Windows

Performance modeling

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