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Social media rumor refutation effectiveness: Evaluation, modelling and enhancement
Information Processing & Management ( IF 8.6 ) Pub Date : 2020-10-23 , DOI: 10.1016/j.ipm.2020.102420
Zongmin Li , Qi Zhang , Xinyu Du , Yanfang Ma , Shihang Wang

Motivated by the practical needs of enhancing social media rumor refutation effectiveness, this paper is dedicated to develop a proper rumor refutation effectiveness index (REI), identify key factors influencing REI and propose decision making suggestions for rumor refutation platforms. 298,118 pieces of comments and 185,209 pieces of the reposters’ verification status of 248 rumor refutation microblogs on Sina Weibo (the Chinese equivalent of Twitter) are collected during a 1-year period using a web crawler. To extract the text characteristics and analyze the sentiment of the rumor refutation microblogs, Natural Language Processing (NLP) approaches are applied. To explore the relationship between REI and the content and contextual factors of the rumor refutation microblogs, four regression models based on the collected data are established, namely linear regression model, Support Vector regression model (SVR), Extreme Gradient Boosting regression model (XGBoostRegressor) and Light Gradient Boosting Machine regression model (LGBMRegressor). The LGBMRegressor has the best goodness-of-fit among the compared regression models. Then, SHapley Additive exPlanations (SHAP) is employed to visualize and explain the LGBMRegressor results. Decision making suggestions for rumor refutation platforms on how to organize rumor refutation microblogs under different situations such as rumor category, author’s influence and heat of topics are proposed.



中文翻译:

社交媒体谣言驳斥有效性:评估,建模和增强

基于增强社交媒体谣言驳斥有效性的实际需求,本文致力于建立适当的谣言驳斥有效性指标([RË一世),找出影响的关键因素 [RË一世并为谣言反驳平台提供决策建议。在1年内,使用网络爬虫收集了298,118条评论和185,209条关于新浪微博上248个谣言驳斥微博的转发者的验证状态。为了提取文本特征并分析谣言驳斥微博的情绪,使用了自然语言处理(NLP)方法。探索之间的关系[RË一世根据谣言反驳微博的内容和语境因素,建立了基于收集到的数据的四个回归模型,分别为线性回归模型,支持向量回归模型(SVR),极端梯度提升回归模型(XGBoostRegressor)和轻度梯度提升机回归。模型(LGBMRegressor)。在比较的回归模型中,LGBMRegressor具有最佳拟合优度。然后,使用SHapley Additive exPlanations(SHAP)可视化并解释LGBMRegressor结果。针对谣言反驳平台在谣言种类,作者的影响力和话题热度等不同情况下如何组织谣言反驳微博的决策建议。

更新日期:2020-10-30
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