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.

辟谣是控制谣言以应对潜在风险的常用方法。本文研究社交媒体辟谣有效性生命周期（SMRREL），重点关注三个重要特征（即寿命、峰值和分布），为（1）增强辟谣有效性的持久性和强度以及（2）提供支持。探讨辟谣有效性的变化规律。共收集官方辟谣平台251条辟谣微博的77080条评论记录、55847条转发记录等相关数据进行分析。为了探索 SMRREL 的寿命和峰值如何受到可能的影响因素的影响，五个回归量（即 RFRegressor、AdaBoostRegressor、XGBoostRegressor、LGBMRegressor、和 CatBoostRegressor）根据收集到的数据进行训练。XGBoostRegressor 显示出最佳性能，并且使用 SHapley Additive exPlanations (SHAP) 显示和解释了结果。为了研究SMRREL的分布，总结了谣言反驳有效性的生命周期图，并将其分为三种类型，即爆发、多峰和陡坡。最后，基于SMRREL分析结果，提出相应的决策建议，以提高辟谣有效性的持久性和强度。谣言反驳有效性的生命周期图被总结并分为三种类型，即爆发型、多峰型和陡坡型。最后，基于SMRREL分析结果，提出相应的决策建议，以提高辟谣有效性的持久性和强度。谣言反驳有效性的生命周期图被总结并分为三种类型，即爆发型、多峰型和陡坡型。最后，基于SMRREL分析结果，提出相应的决策建议，以提高辟谣有效性的持久性和强度。