TY - GEN

T1 - The Influence of Social Factors on Mental Health and Wellbeing during the COVID-19 Pandemic

AU - El-Haj, Mahmoud

AU - Sartain, Alex

N1 - ©2022 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2022/1/13

Y1 - 2022/1/13

N2 - Abstract— This study uses Natural Language Processing and Machine Learning techniques to understand the effect of the COVID-19 pandemic on mental wellbeing. We considered different user groups and locations in the USA to analyze the influence contrasting social factors, such as political stance, had on wellbeing. We measured the mental wellbeing of the social media users through understanding negative sentiment and mental health topic discussion in Twitter posts added by users from the top 10 Democrat and top 10 Republican cities in the USA. To measure the topic discussion, we used a mental health keyword list and developed machine learning models to classify the topic of a tweet. The primary findings include the similarity of the effect the pandemic had on Republican and Democrat cities when considering a timeline of tweets, whilst an increase in ‘Anxiety’ was discussed across different user groups and cities. Enforcement strategies had aninfluence on mental wellbeing with results differing for Republican and Democrat cities. An accurate text classifier was developed and used to categorize tweets into different mental health topics. The results showed how medical and unemployed users discussed topics like ‘anxiety’ and‘depression’ more than a control set of users. The best machine learning model was developed using a Decision Tree algorithm which achieved an accuracy of 87% on unseen data.

AB - Abstract— This study uses Natural Language Processing and Machine Learning techniques to understand the effect of the COVID-19 pandemic on mental wellbeing. We considered different user groups and locations in the USA to analyze the influence contrasting social factors, such as political stance, had on wellbeing. We measured the mental wellbeing of the social media users through understanding negative sentiment and mental health topic discussion in Twitter posts added by users from the top 10 Democrat and top 10 Republican cities in the USA. To measure the topic discussion, we used a mental health keyword list and developed machine learning models to classify the topic of a tweet. The primary findings include the similarity of the effect the pandemic had on Republican and Democrat cities when considering a timeline of tweets, whilst an increase in ‘Anxiety’ was discussed across different user groups and cities. Enforcement strategies had aninfluence on mental wellbeing with results differing for Republican and Democrat cities. An accurate text classifier was developed and used to categorize tweets into different mental health topics. The results showed how medical and unemployed users discussed topics like ‘anxiety’ and‘depression’ more than a control set of users. The best machine learning model was developed using a Decision Tree algorithm which achieved an accuracy of 87% on unseen data.

KW - COVID-19

KW - Mental Health

KW - Wellbeing

KW - Pandemic

KW - Natural Language Processing

KW - machine learning

U2 - 10.1109/BigData52589.2021.9671551

DO - 10.1109/BigData52589.2021.9671551

M3 - Conference contribution/Paper

SN - 9781665445993

SP - 2818

EP - 2827

BT - 2021 IEEE International Conference on Big Data (Big Data)

PB - IEEE

T2 - IEEE International Conference on Big Data

Y2 - 15 December 2021 through 18 December 2021

ER -