Work-Stress Content Analysis Using Social Media Data

Abstract

Since its occurrence in December 2019, COVID-19 has adversely affected both the personal and professional lives of people across the world. The widespread continuance of the pandemic has increased feelings of stress among people. Focusing on content analysis of data collected from Twitter, a social media platform, the current article aims at identifying and analyzing job-related stress among the masses with a focus on two primary terms related to stress among working people—employment and unemployment. A total of 32,237 tweets were downloaded from locations of four major cities of India, namely, Delhi, Mumbai, Kolkata and Chennai based on the keywords used for the study. Content analysis using R was employed as the technique to study the correlation and association of terms to find linkages between feelings/sentiments shared by the masses. Two clusters (Speculative and Misfit-Originators) of job-related stress causes were identified and coping strategies were suggested based on the reasons for stress in the different clusters. The findings suggest that increasing the perception of volition and allaying fears act as coping strategies for employees.