Expressions of emotions are common in news posts on social media. News providers embed emotional expressions to grab users’ attention and entice them to read the full article. However, there is a lack of empirical evidence to support this practice. We develop a theoretical model using emotions as social information theory to explain how, when and why the arousal of emotions expressed in headlines influences news article reading in social media. Through three experiments, we provide converging evidence that the use of expressed arousal backfires and reduces news reading. We also reveal a context-dependent boundary condition (i.e., information gap) and explore underlying mechanisms. Our findings speak to the growing literature on emotional expressions in social media and challenge the assumption that expressed arousal is beneficial in increasing news readership in social media.
Kadian, Arjun; Yin, Dezhi; and Steele, Logan, "Emotional Arousal and News Readership in Social Media" (2022). SIGHCI 2022 Proceedings. 2.