Assessing vulnerability to psychological distress during the COVID-19 pandemic through the analysis of microblogging content.
Future Gener Comput Syst
; 125: 446-459, 2021 Dec.
Article
in English
| MEDLINE | ID: covidwho-1284093
ABSTRACT
In recent years we have witnessed a growing interest in the analysis of social media data under different perspectives, since these online platforms have become the preferred tool for generating and sharing content across different users organized into virtual communities, based on their common interests, needs, and perceptions. In the current study, by considering a collection of social textual contents related to COVID-19 gathered on the Twitter microblogging platform in the period between August and December 2020, we aimed at evaluating the possible effects of some critical factors related to the pandemic on the mental well-being of the population. In particular, we aimed at investigating potential lexicon identifiers of vulnerability to psychological distress in digital social interactions with respect to distinct COVID-related scenarios, which could be "at risk" from a psychological discomfort point of view. Such scenarios have been associated with peculiar topics discussed on Twitter. For this purpose, two approaches based on a "top-down" and a "bottom-up" strategy were adopted. In the top-down approach, three potential scenarios were initially selected by medical experts, and associated with topics extracted from the Twitter dataset in a hybrid unsupervised-supervised way. On the other hand, in the bottom-up approach, three topics were extracted in a totally unsupervised way capitalizing on a Twitter dataset filtered according to the presence of keywords related to vulnerability to psychological distress, and associated with at-risk scenarios. The identification of such scenarios with both approaches made it possible to capture and analyze the potential psychological vulnerability in critical situations.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Experimental Studies
/
Prognostic study
Language:
English
Journal:
Future Gener Comput Syst
Year:
2021
Document Type:
Article
Affiliation country:
J.future.2021.06.044
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