Duration: October 2023 to September 2026
Conductor: Prof. Dr. Thorsten Quandt (University Münster), Prof. Dr. Annie Waldherr (University Vienna)
Assistance: Svenja Boberg, M.A., Sofie Beisemann, M.A.
Funding: DFG (WEAVE)
Keywords: online communication, social issues, topics, emergence, virality, interview study, computational content analysis
In the ongoing social debate about social issues, hashtag campaigns such as #metoo or #blacklivesmatter have gained massive importance in recent years. They draw attention to a socially relevant problem (such as sexism or racism) and position it as a social issue. This social issue is shared (using hashtags) on social media and spreads virally. Once the social issue has gone viral, it attracts the attention of traditional media, which make the addressed social problem accessible to a broad public and to the discourse of society. This process is called social issue emergence (SIE) and takes place in the hybrid media system with interaction between analog and digital media logic. But what are the dynamics behind this process? To what extent can actors control and influence the career of a social issue? And what role does the seemingly uncontrolled viral spread of social issues play? The goal of the project is to develop a model based on theoretical and empirical findings that can be used to reconstruct, simulate, understand, and predict the dynamics of social issue emergence.
The aim of the research project is, on the one hand, to develop a deep theoretical understanding of social issue emergence that takes into account the dynamics and interaction between micro-, meso-, and macro-processes. On the other hand, the empirical investigation of the logic and characteristics of SIE shall be enabled by applying innovative, cutting-edge computational methods. For this purpose, guided interviews with social issue actors, politicians and journalists will be conducted, which will for instance provide information about influencing factors and typical SIE trajectories. In addition, automated content analyses will be conducted with data from social media, alternative media and news media. A particular focus is on longitudinal analyses of the data via dynamic network analyses and time series, to be able to make statements about the emergence, development and disappearance of competing topics over time, for example. For the modeling of SIE dynamics, Agent Based Modeling (ABM) is used, with the help of which conclusions can be drawn about causal influences and correlations of the observable SIE dynamics.