Conference proceeding
Identification of Rumor Refuters Based on an Explainable Machine Learning Framework
The Eighteenth International Conference on Management Science and Engineering Management, v 215, pp 741-752
04 Aug 2024
Abstract
Facing of the existing reception dilemma of rumor refuting information, crowd identification and feature analysis should be realized through big data analysis. Our approach aims to accurately identify rumor refuters and interpret predictions. Initially, we compare six machine learning models, with results demonstrating the superiority of eXtreme Gradient Boosting (XGBoost) over other advanced models. Subsequently, we introduce Shapley additive explanations (SHAP) to interpret the predictions of complex machine learning models and assess the importance of various features. Our findings underscore that utilizing XGBoost alongside the SHAP approach can offer decision support, enhancing the effectiveness of rumor governance.
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Details
- Title
- Identification of Rumor Refuters Based on an Explainable Machine Learning Framework
- Creators
- Zongmin Li - Sichuan UniversityYujie Zhou - Sichuan UniversityWenjing Shen - Drexel UniversityLiming Zhang (Corresponding Author) - Sichuan University
- Publication Details
- The Eighteenth International Conference on Management Science and Engineering Management, v 215, pp 741-752
- Series
- Lecture Notes on Data Engineering and Communications Technologies
- Publisher
- Springer Nature
- Number of pages
- 12
- Grant note
- 72174134 / National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Decision Sciences (and Management Information Systems)
- Web of Science ID
- WOS:001323495500052
- Scopus ID
- 2-s2.0-85202028874
- Other Identifier
- 991022202487904721