TY - JOUR
T1 - Depression at Work: Exploring Depression in Major US Companies from Online Reviews
AU - Sen, Indira
AU - Quercia, Daniele
AU - Constantinides, Marios
AU - Montecchi, Matteo
AU - Capra, Licia
AU - Scepanovic, Sanja
AU - Bianchi, Renzo
PY - 2022/5/22
Y1 - 2022/5/22
N2 - Studies on depression in the workplace have mostly investigated its impact on individual employees. Little is known about its association with the company as a whole, or the state where the company is based. This is due to the lack of scalable methodologies operationalizing depression in the specific context of the workplace, and of data documenting potential distress. In this work, we adapted a work-related depression scale called Occupational Depression Inventory (ODI), gathered more than 350K employee reviews of 104 major companies across the whole US for the (2008-2020) years, and developed a deep-learning framework (called AutoODI) scoring these reviews on a composite ODI score. Presence of ODI mentions manifested itself not only at micro-level (companies scoring high in ODI suffered from low stock growth) but also at macro-level (states hosting these companies were associated with high depression rates, talent shortage, and economic deprivation). This new way of applying AutoODI onto company reviews offers both theoretical implications for the literature in computational social science, occupational health and economic geography, and practical implications for companies and policy makers.
AB - Studies on depression in the workplace have mostly investigated its impact on individual employees. Little is known about its association with the company as a whole, or the state where the company is based. This is due to the lack of scalable methodologies operationalizing depression in the specific context of the workplace, and of data documenting potential distress. In this work, we adapted a work-related depression scale called Occupational Depression Inventory (ODI), gathered more than 350K employee reviews of 104 major companies across the whole US for the (2008-2020) years, and developed a deep-learning framework (called AutoODI) scoring these reviews on a composite ODI score. Presence of ODI mentions manifested itself not only at micro-level (companies scoring high in ODI suffered from low stock growth) but also at macro-level (states hosting these companies were associated with high depression rates, talent shortage, and economic deprivation). This new way of applying AutoODI onto company reviews offers both theoretical implications for the literature in computational social science, occupational health and economic geography, and practical implications for companies and policy makers.
M3 - Article
SN - 2573-0142
JO - Proceedings of the ACM on Human-Computer Interaction
JF - Proceedings of the ACM on Human-Computer Interaction
ER -