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

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. © 2022, Emerald Publishing Limited.

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

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.

Student perspective-based evaluation of online transition during the COVID-19 outbreak: A case study of PNU students

Due to the COVID-19 pandemic, many higher education institutes shifted to online learning with the precautionary measures taken by governments. This transition was very rapid and sudden, which brought challenges to all learning methods in all disciplines while opening up new opportunities. Different studies have been carried out to evaluate experiences of online migration and study its effect on stakeholders in education. This paper is aimed to rationally evaluate the transition to online learning in PNU from the student perspective. Five thousand ten student responses to an online survey were collected. The survey results indicate that the majority of students were satisfied by the quality of the delivered courses during this crisis period as they have received adequate support from instructors, IT, and leaders. Moreover, student satisfaction can be explained by the readiness and preparedness of PNU for such circumstances. Indeed, students and instructors are poised to adopt new learning modalities as they were familiar with new technologies and innovation in learning and teaching so far. © 2021 IGI Global. All rights reserved.