Kusuma Ikhwan Yuda, Visnyovszki Ádám, Bahar Muh Akbar
Institute of Clinical Pharmacy, University of Szeged, 6725 Szeged, Hungary.
Pharmacy Study Program, Faculty of Health, Universitas Harapan Bangsa, 53182 Purwokerto, Indonesia.
Explor Res Clin Soc Pharm. 2024 Oct 9;16:100521. doi: 10.1016/j.rcsop.2024.100521. eCollection 2024 Dec.
Mpox, a zoonotic disease re-emerging from animals to humans, poses a risk of evolving into a global pandemic due to its high infectivity and potential asymptomatic transmission. This study maps the structure and configuration of mpox-related discussions on Twitter/X, identifies key influencers and top hashtags, and analyzes public sentiment. Data were collected using NodeXL Pro from May 7, 2022, to January 15, 2023, with the keyword "Monkeypox" and visualized using Gephi. Social network analysis ranked nodes by betweenness centrality scores to highlight key communicators, and the YifanHu layout algorithm visualized the network. Influential users, source topics, and hashtags were identified, and sentiment analysis was conducted using Azure Machine Learning tools. The analysis identified 11,397 mpox-related tweets. The network structure resembled a community with diverse participants. Influential users included health and science journalists, writers, academics, medical doctors, and public figures. News media and organizational websites were the top information sources, emphasizing the need for accessible scientific information. "Monkeypox" and "Mpox" were the most prevalent hashtags. Negative sentiments dominated the discussion. This analysis provides insights into network structure, key influencers, information sources, and public sentiment, aiding tailored health initiatives to address misinformation and advocate valid health information and emergency responses.
猴痘是一种从动物传播至人类的人畜共患病，因其高传染性和潜在的无症状传播，有演变为全球大流行疾病的风险。本研究绘制了推特/ X上与猴痘相关讨论的结构和形态，识别了关键影响者和热门话题标签，并分析了公众情绪。使用NodeXL Pro于2022年5月7日至2023年1月15日收集数据，关键词为“猴痘”，并使用Gephi进行可视化展示。社交网络分析根据中介中心性得分对节点进行排名，以突出关键传播者，YifanHu布局算法将网络可视化。识别了有影响力的用户、源主题和话题标签，并使用Azure机器学习工具进行了情绪分析。分析确定了11397条与猴痘相关的推文。网络结构类似于一个有不同参与者的社区。有影响力的用户包括健康和科学记者、作家、学者、医生和公众人物。新闻媒体和组织网站是主要信息来源，这凸显了获取科学信息的必要性。“猴痘”和“Mpox”是最常见的话题标签。负面情绪在讨论中占主导地位。该分析提供了关于网络结构、关键影响者、信息来源和公众情绪的见解，有助于制定针对性的健康举措，以应对错误信息，并倡导有效的健康信息和应急响应。
