Twitter use by the dementia community during COVID-19: a user classification and social network analysis

Fatimah Alhayan, D. Pennington, S. Ayouni
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引用次数: 5

Abstract

PurposeThe study aimed to examine how different communities concerned with dementia engage and interact on Twitter.Design/methodology/approachA dataset was sampled from 8,400 user profile descriptions, which was labelled into five categories and subjected to multiple machine learning (ML) classification experiments based on text features to classify user categories. Social network analysis (SNA) was used to identify influential communities via graph-based metrics on user categories. The relationship between bot score and network metrics in these groups was also explored.FindingsClassification accuracy values were achieved at 82% using support vector machine (SVM). The SNA revealed influential behaviour on both the category and node levels. About 2.19% suspected social bots contributed to the coronavirus disease 2019 (COVID-19) dementia discussions in different communities.Originality/valueThe study is a unique attempt to apply SNA to examine the most influential groups of Twitter users in the dementia community. The findings also highlight the capability of ML methods for efficient multi-category classification in a crisis, considering the fast-paced generation of data.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2021-0208.
COVID-19期间痴呆症社区使用Twitter:用户分类和社交网络分析
目的:该研究旨在研究不同的痴呆症社区如何在Twitter上参与和互动。设计/方法/方法从8400个用户简介描述中抽取数据集,将其划分为5个类别,并进行基于文本特征的多个机器学习(ML)分类实验,对用户类别进行分类。使用社会网络分析(SNA)通过基于用户类别的图形度量来确定有影响力的社区。还探讨了这些组中机器人得分和网络指标之间的关系。使用支持向量机(SVM)的分类准确率达到82%。SNA揭示了在类别和节点级别上的有影响力的行为。在不同的社区中,约有2.19%的人怀疑社交机器人导致了2019冠状病毒病(COVID-19)痴呆的讨论。独创性/价值这项研究是一个独特的尝试,应用SNA来检查痴呆症社区中最有影响力的Twitter用户群体。研究结果还强调了ML方法在危机中高效多类别分类的能力,考虑到数据的快速生成。同行评议本文的同行评议历史可在:https://publons.com/publon/10.1108/OIR-04-2021-0208。
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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