The Generative Side of Boredom: Evidence from Twitter Data Analysis

Boredom has a generative aspect that has been well-documented. However, it is an aspect of boredom that is less often explored empirically, since generativity is difficult to observe and measure. To remedy this, this study provides an analytical case study of boredom as observed in interaction over Twitter in hopes of illustrating this generative dimension. In so doing, this study measures and analyzes the varying degrees of generativity of tweets that involve the emotion of boredom. Across different degrees of generativity, I investigate the changing use of emotional and relational processes in these tweets. The findings suggest that positive emotion is a stronger predictor for generativity, compared to overall affect, negative emotion, and specific emotions like: anger, sadness, and anxiety. In addition, the findings suggest that generativity of tweets rises when the focuses is directed at others rather than the self. These findings are inconsistent with previous research that sees boredom as having a solely negative and asocial dimension. Given the fact that current research on boredom sees it as a highly individualistic emotion, with a narrow set of problematic consequences, this paper provides a compelling case study to examine how social network sites create new possibilities and directions for emotional expressions.