Using Natural Language Processing to Explore Mental Health Insights From UK Tweets During the COVID-19 Pandemic: Infodemiology Study

Abstract

Background There is need to consider the value of soft intelligence, leveraged using accessible natural language processing (NLP) tools, as a source of analyzed evidence to support public health research outputs and decision-making. Objective The aim of this study was to explore the value of soft intelligence analyzed using NLP. As a case study, we selected and used a commercially available NLP platform to identify, collect, and interrogate a large collection of UK tweets relating to mental health during the COVID-19 pandemic. Methods A search strategy comprised of a list of terms related to mental health, COVID-19, and lockdown restrictions was developed to prospectively collate relevant tweets via Twitter’s advanced search application programming interface over a 24-week period. We deployed a readily and commercially available NLP platform to explore tweet frequency and sentiment across the United Kingdom and identify key topics of discussion. A series of keyword filters were used to clean the initial data retrieved and also set up to track specific mental health problems. All collated tweets were anonymized. Results We identified and analyzed 286,902 tweets posted from UK user accounts from July 23, 2020 to January 6, 2021. The average sentiment score was 50%, suggesting overall neutral sentiment across all tweets over the study period. Major fluctuations in volume (between 12,622 and 51,340) and sentiment (between 25% and 49%) appeared to coincide with key changes to any local and/or national social distancing measures. Tweets around mental health were polarizing, discussed with both positive and negative sentiment. Key topics of consistent discussion over the study period included the impact of the pandemic on people’s mental health (both positively and negatively), fear and anxiety over lockdowns, and anger and mistrust toward the government. Conclusions Using an NLP platform, we were able to rapidly mine and analyze emerging health-related insights from UK tweets into how the pandemic may be impacting people’s mental health and well-being. This type of real-time analyzed evidence could act as a useful intelligence source that agencies, local leaders, and health care decision makers can potentially draw from, particularly during a health crisis.

Investigating Social Media to Evaluate Emergency Medicine Physicians’ Emotional Well-being During COVID-19

“…First, self-identified EM physicians and resident physicians were posting large amounts of content throughout the periods before and during the COVID-19 pandemic. As for other populations, Twitter serves as a large source for this content and a proxy for the topics and language used related to mental health . Social media platforms provide unique and dynamic environments for users to share their personal opinions and thoughts and spaces to collect data at scale.…”

“…As for other populations, Twitter serves as a large source for this content and a proxy for the topics and language used related to mental health. 16 , 20 , 28 , 29 Social media platforms provide unique and dynamic environments for users to share their personal opinions and thoughts and spaces to collect data at scale. We were able to identify physicians and resident physicians on Twitter using traditional methods.…”

Comparative Topic Modeling for Determinants of Divergent Report Results Applied to Macular Degeneration Studies

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Sentiment Analysis of Insomnia-Related Tweets via a Combination of Transformers Using Dempster-Shafer Theory: Pre– and Peri–COVID-19 Pandemic Retrospective Study

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