Revisiting Deep Learning-based Rumor Detection Models with Interpretable Tools

被引：0

作者：

He G. ^{[1
]}

Ren J. ^{[1
]}

Li Z. ^{[1
]}

Lin C. ^{[1
]}

Yu H. ^{[1
]}

机构：

[1] Faculty of Economics and Management, East China Normal University, Shanghai

来源：

Data Analysis and Knowledge Discovery | 2024年 / 8卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Deep Learning; Interpretable Machine Learning; LIME; Rumor Detection; SHAP;

D O I：

10.11925/infotech.2096-3467.2023.0684

中图分类号：

学科分类号：

摘要：

[Objective] This study explores whether content-based deep detection models can identify the semantics of rumors. [Methods] First, we use the BERT model to identify the key features of rumors from benchmark datasets in Chinese and English. Then, we utilized two interpretable tools, LIME, based on local surrogate models, and SHAP, based on cooperative game theory, to analyze whether these features can reflect the nature of rumors. [Results] The key features calculated by the interpretable tools on different models and datasets showed significant differences, and it is challenging to decide the semantic relationship between the features and rumors. [Limitations] The datasets and models examined in this study need to be expanded. [Conclusion] Deep learning-based rumor detection models only work with the features of the training set and lack sufficient generalization and interpretability for diverse real-world scenarios. © 2024 Chinese Academy of Sciences. All rights reserved.

引用

页码：1 / 13

页数：12

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