A Novel Graph-Based Approach to Identify Opinion Leaders in Twitter

IF 4.5 2区计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS

IEEE Transactions on Computational Social Systems Pub Date : 2025-01-13 DOI:10.1109/TCSS.2024.3455415

Marco Furini;Luca Mariotti;Riccardo Martoglia;Manuela Montangero

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引用次数: 0

Abstract

This study explores the influence of social media on health-related discourse amid the COVID-19 pandemic, focusing on Italian-language tweets posted on Twitter from March 2020 to December 2021. Analyzing a dataset comprising 13 million tweets, the research addresses three key questions: who emerged as opinion leaders on Twitter during the pandemic in Italy?; did health institutions in Italy successfully establish themselves as opinion leaders?; and how did the content of COVID-19-related tweets in Italy evolve over time? Employing a custom-designed graph and the personalized PageRank algorithm, the study identifies opinion leaders on Twitter. Additionally, psycholinguistic analysis provides insights into the content, themes, and emotional undertones of the tweets. The findings of this research contribute to a deeper understanding of social media's influence on public opinion and behavior during the pandemic. Furthermore, they offer valuable insights for public health officials and policymakers seeking to address health-related issues on social media platforms.

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一种基于图表的Twitter意见领袖识别方法

本研究探讨了2019冠状病毒病大流行期间社交媒体对健康相关话语的影响，重点关注2020年3月至2021年12月在推特上发布的意大利语推文。该研究分析了一个包含1300万条推文的数据集，解决了三个关键问题：在意大利疫情期间，谁成为推特上的意见领袖？意大利的卫生机构是否成功地确立了自己的意见领袖地位？意大利与covid -19相关的推文内容是如何随着时间的推移而演变的？该研究采用定制设计的图表和个性化的PageRank算法，确定了Twitter上的意见领袖。此外，心理语言学分析提供了对推文内容、主题和情感暗示的见解。这项研究的结果有助于更深入地了解社交媒体在大流行期间对公众舆论和行为的影响。此外，它们为寻求在社交媒体平台上解决与健康相关问题的公共卫生官员和政策制定者提供了宝贵的见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Computational Social Systems Social Sciences-Social Sciences (miscellaneous)

CiteScore

10.00

自引率

20.00%

发文量

316

期刊介绍： IEEE Transactions on Computational Social Systems focuses on such topics as modeling, simulation, analysis and understanding of social systems from the quantitative and/or computational perspective. "Systems" include man-man, man-machine and machine-machine organizations and adversarial situations as well as social media structures and their dynamics. More specifically, the proposed transactions publishes articles on modeling the dynamics of social systems, methodologies for incorporating and representing socio-cultural and behavioral aspects in computational modeling, analysis of social system behavior and structure, and paradigms for social systems modeling and simulation. The journal also features articles on social network dynamics, social intelligence and cognition, social systems design and architectures, socio-cultural modeling and representation, and computational behavior modeling, and their applications.