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• W4296642214 endingPage "103077" @default.
• W4296642214 startingPage "103077" @default.
• W4296642214 abstract "• Studying the social media rumor refutation effectiveness lifecycle (SMRREL) from three important aspects (i.e., lifespan, peak value, and distribution). • Exploring possible factors affecting the lifespan and peak value of SMRREL through regression models in the machine learning field (e.g., XGBoostRegressor, LGBMRegressor, CatBoostRegressor, and other ensemble algorithms) and SHapley Additive Explanations method. • Summarizing distributions of SMRREL with the help of the K-shape clustering algorithm. • Giving relevant decision-making suggestions to enhance the persistence and intensity of rumor refutation effectiveness. Rumor refutation is a common method to control rumors to address potential risks. This paper studies the social media rumor refutation effectiveness lifecycle (SMRREL), focusing on three important characteristics (i.e., lifespan, peak value, and distribution) to provide support for (1) enhancing the persistence and intensity of rumor refutation effectiveness and (2) investigating the changing law of rumor refutation effectiveness. In total, 77,080 comment records, 55,847 forward records, and other pertinent data of 251 rumor refutation microblogs from an official rumor refutation platform are collected to perform analysis. To explore how the lifespan and peak value of SMRREL are influenced by the possible affecting factors, five regressors (i.e., RFRegressor, AdaBoostRegressor, XGBoostRegressor, LGBMRegressor, and CatBoostRegressor) are trained based on the collected data. The XGBoostRegressor shows the best performance, and the results are shown and explained using SHapley Additive exPlanations (SHAP). To investigate the distribution of SMRREL, lifecycle graphs of rumor refutation effectiveness are summarized and divided into three types, i.e., Outburst, Multiple Peaks, and Steep Slope. Finally, based on the results of the SMRREL analysis, corresponding decision-making recommendations are proposed to make better persistence and intensity of rumor refutation effectiveness." @default.
• W4296642214 created "2022-09-23" @default.
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• W4296642214 date "2022-11-01" @default.
• W4296642214 modified "2023-10-18" @default.
• W4296642214 title "Lifecycle research of social media rumor refutation effectiveness based on machine learning and visualization technology" @default.
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• W4296642214 doi "https://doi.org/10.1016/j.ipm.2022.103077" @default.
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