数字时代的州长:利用机器学习分析和预测日本 COVID-19 大流行期间的社交媒体参与情况

IF 3.4 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Salama Shady, Vera Paola Shoda, Takashi Kamihigashi
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引用次数: 0

摘要

本文全面分析了 COVID-19 大流行期间日本县知事在社交媒体上发布的帖子。它研究了社交媒体活动水平、知事特征和参与度指标之间的相关性。为了预测特定推文的公民参与度,使用三个特征集训练了机器学习模型(MLM)。第一组包括代表个人资料和推文相关特征的变量。第二组包含来自三个流行模型的词嵌入,而第三组则将第一组与其中一个嵌入相结合。此外,还使用了七个分类器。表现最好的模型采用了第一组特征与 FastText 嵌入和 XGBoost 分类器。本研究旨在收集州长的 COVID-19 相关推文,分析参与度指标,研究与州长特征的相关性,检查推文相关特征,并训练 MLM 进行预测。本文的主要贡献有两个方面。首先,本文分析了都道府县知事在 COVID-19 大流行期间的社交媒体参与情况,揭示了他们的传播策略和公民参与结果。其次,它探讨了多词模型和词嵌入在预测推文参与度方面的有效性,为危机传播中的政策制定者提供了实际意义。研究结果强调了社交媒体参与对有效治理的重要性,并深入探讨了影响公民参与的因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Governors in the Digital Era: Analyzing and Predicting Social Media Engagement Using Machine Learning during the COVID-19 Pandemic in Japan
This paper presents a comprehensive analysis of the social media posts of prefectural governors in Japan during the COVID-19 pandemic. It investigates the correlation between social media activity levels, governors’ characteristics, and engagement metrics. To predict citizen engagement of a specific tweet, machine learning models (MLMs) are trained using three feature sets. The first set includes variables representing profile- and tweet-related features. The second set incorporates word embeddings from three popular models, while the third set combines the first set with one of the embeddings. Additionally, seven classifiers are employed. The best-performing model utilizes the first feature set with FastText embedding and the XGBoost classifier. This study aims to collect governors’ COVID-19-related tweets, analyze engagement metrics, investigate correlations with governors’ characteristics, examine tweet-related features, and train MLMs for prediction. This paper’s main contributions are twofold. Firstly, it offers an analysis of social media engagement by prefectural governors during the COVID-19 pandemic, shedding light on their communication strategies and citizen engagement outcomes. Secondly, it explores the effectiveness of MLMs and word embeddings in predicting tweet engagement, providing practical implications for policymakers in crisis communication. The findings emphasize the importance of social media engagement for effective governance and provide insights into factors influencing citizen engagement.
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来源期刊
Informatics
Informatics Social Sciences-Communication
CiteScore
6.60
自引率
6.50%
发文量
88
审稿时长
6 weeks
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