The special issue collects four articles that review state-of-the-art computational methods for media effects research. It was edited by J. Loes Pouwels (Radboud University), Theo Araujo (University of Amsterdam), Wouter van Atteveldt (VU Amsterdam), Marko Bachl (FU Berlin) & Patti M. Valkenburg (University of Amsterdam).

The abstract to our editorial summarizes the Special Issue:

A pressing societal and scientific question is how social media use affects our cognitions, emotions, and behaviors. To answer this question, fine-grained insight into the content of individuals’ social media use is needed. It is difficult to study content-based social media effects with traditional survey methods because such methods are incapable of capturing the extreme volume and variety of social media content that is shared and received. Therefore, this special issue aims to illustrate how content-based social media effects could be examined by integrating communication sciences and computational methods. We describe a three-step method to investigate content-based media effects, which involves (a) collecting digital trace data, (b) performing automated textual and visual content analysis, and (c) conducting linkage analysis. This Special Issue zooms in on these steps and describes the strengths and weaknesses of different computational methods. We conclude with some challenges that need to be addressed in future research.

Pouwels, J. L., Araujo, T., van Atteveldt, W., Bachl, M., & Valkenburg, P. M. (2024). Integrating Communication Science and Computational Methods to Study Content-Based Social Media Effects. Communication Methods and Measures, 18(2), 115–123. https://doi.org/10.1080/19312458.2023.2285766