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Quiet quitting is a term popularized on social media describing workers intentionally doing only the essential or minimum requirements of a job. Recent research on quiet quitting has focused on attaching the phenomenon to prior theoretical research on beliefs, attitudes, and workplace behaviors. This study aimed to expand this literature through identifying emotion labels and associated topic in online discussion on quiet quitting. Top-level comments on three YouTube videos about quiet quitting were analyzed through semantic network analysis. Traditional sentiment analysis does not separate emotion-objects from emotional content. As such, emotion labels from Plutchik’s emotion wheel were identified, and network statistics and cluster analysis were used to associate words with these emotion labels. This allowed for avoiding double-counting potential emotion-objects as indicative of emotions as well. Interest, love, and joy were the most central emotion labels mentioned in the comments, often mentioned with work, job, people, and life. The cluster analysis identified eight themes connecting emotion labels and associated words. The findings suggest that both positive and negative emotions are associated with societal changes and work life.
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Barber et al. (2026) studied this question.
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