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

Abstract

Purpose: The study aimed to examine how different communities concerned with dementia engage and interact on Twitter. Design/methodology/approach: A 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. Findings: Classification 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/value: The 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 review: The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2021-0208.