This study examines the impact of discrete emotional expression (i.e., expression of anxiety, sadness, anger, disgust, love, joy, surprise, and anticipation) on the differential diffusion of online content in social media networks. We conducted an analysis on a random sample of 387,486 online articles and their corresponding diffusion cascades, involving more than six million unique individuals, on a major online social networking platform. Our investigation focused on the relationships between discrete emotional expression and the diffusion of online articles, specifically the structural properties of diffusion cascades, such as size, depth, maximum breadth, and structural virality. We employed various econometric model specifications, and our results robustly demonstrate that articles expressing higher levels of anxiety, love, and surprise reach a larger number of individuals and diffuse more deeply, broadly, and virally. In contrast, expression of anger, sadness, and joy exhibit the opposite effect. Additionally, we find that articles with different emotional expression tend to spread differently based on individual characteristics and social ties. Our findings offer valuable insights into the diffusion and regulation of online content from the perspectives of emotional expression and social networks.
One advantage of advertising on social media is leveraging users’ expression of “likes” to influence the perceptions and responses of others in their network. Through a largescale field experiment on WeChat, three online lab studies and a theoretical model, we explore whether and how displaying more “likes” in an ad can effectively lead to more ad “likes” and clicks. We find that displaying the first “like” can significantly increase users’ tendencies to both “like” and click on an ad. However, on average, showing additional “likes” does not further increase the clicking propensity, although it consistently attracts more “likes.” We further find that displaying more “likes” increases the clickthrough rate for lesser-known brands but not for well-known brands, and has a stronger impact on the “like” rate for more socially engaged users than for less socially engaged ones. These findings are consistent with the interplay between informational and normative social influences in social advertising. The public visibility of “likes” makes liking more susceptible to normative social influence than clicking. The coexistence of these two forces can lead to an enhanced conformity effect on liking and a crowding-out effect on clicking. Our findings offer novel implications for managing social advertising and designing social media platforms.