Real-time assessment of motives for sharing and creating content among highly active Twitter users
Abstract
What motivates people to share and create content online? In real time, we linked each of N= 2, 762 individual posts (retweets and newly created content) with the self-reported motives from a sample of N= 137 highly active US Twitter users over the course of one week. We also captured their total activity of N= 48, 419 posts over 10 weeks (March-May 2022). Our results reveal that sharing (retweeting) political content stemmed mostly from motives related to expression and identity. When creating content, participants were more likely to be motivated by the goals of informing and persuading others, for which they used negative language and expressed outrage. In contrast, entertaining content and positive language was used for socializing and attention. Original and political content featuring outrage and anger was more likely to be subsequently retweeted by others. These findings may denote adaptive strategies in the incentive structure of social media that rewards such content.
Marginal effect comparison of model estimates for content-feature variables of created (original tweets, replies, and quotes) posts. A: Results of the logistic regression model 2 for political and entertainment content, outrage, sentiment (coded negative to positive), the top three emotions, and presence of an external URL. B: Resulting marginal effects of the hurdle lognormal model 3 for subsequent retweets per category of motive. C: Resulting marginal contrasts from the hurdle lognormal model for the full sample of posts containing original content.
Philipp Lorenz-Spreen
Junior Research Group Leader
My research interests are causal inference in dynamic time series systems