Version 1
: Received: 19 January 2022 / Approved: 21 January 2022 / Online: 21 January 2022 (12:59:09 CET)
Version 2
: Received: 4 February 2022 / Approved: 8 February 2022 / Online: 8 February 2022 (11:12:14 CET)
How to cite:
Hermann, C.; Govender, M. eHealth-Engagement on Facebook during COVID-19: A Netnographical Data Visualization. Preprints2022, 2022010323. https://doi.org/10.20944/preprints202201.0323.v1
Hermann, C.; Govender, M. eHealth-Engagement on Facebook during COVID-19: A Netnographical Data Visualization. Preprints 2022, 2022010323. https://doi.org/10.20944/preprints202201.0323.v1
Hermann, C.; Govender, M. eHealth-Engagement on Facebook during COVID-19: A Netnographical Data Visualization. Preprints2022, 2022010323. https://doi.org/10.20944/preprints202201.0323.v1
APA Style
Hermann, C., & Govender, M. (2022). eHealth-Engagement on Facebook during COVID-19: A Netnographical Data Visualization. Preprints. https://doi.org/10.20944/preprints202201.0323.v1
Chicago/Turabian Style
Hermann, C. and Melanie Govender. 2022 "eHealth-Engagement on Facebook during COVID-19: A Netnographical Data Visualization" Preprints. https://doi.org/10.20944/preprints202201.0323.v1
Abstract
Understanding social media networks and group interactions are crucial to the advancement of linguistic and cultural behaviour. This includes the manner in which people accessed advice on health, especially during the global lockdown periods. Some people turned to social media to access information on health where most activities were curtailed with isolation rules, especially for older generations. Facebook public pages, groups and verified profiles, using "senior citizen health", "older generations", and "healthy living" keywords were analysed over a 12-month period to analyse the engagement promoting good mental health. CrowdTangle was used to source English language status updates, photo and video sharing information which resulted in an initial 116,321 posts and 6,462,065 interactions Data analysis and visualisation were used to explore large datasets, including natural language processing for “Message” content discovery, word frequency and correlational analysis and co-word clustering. Preliminary results indicate strong links to healthy aging information shared on social media which showed correlations to global daily confirmed case and daily death totals. The results can be used to identify public concerns early on and address mental health issues in the senior generation on Facebook.
social media; netnography; mental health; natural language processing; visualization; data analysis; COVID-19
Subject
Social Sciences, Psychology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
