Advancing automated content analysis for a new era of media effects research: The key role of transfer learning

The availability of individual-level digital trace data offers exciting new ways to study media uses and effects based on the actual content that people encountered. In this article, we argue that to really reap the benefits of this data, we need to update our methodology for automated text analysis. We review challenges for the automatic identification of theoretically relevant concepts in texts along three dimensions: format/style, language, and modality. These dimensions unveil a significantly higher level of diversity and complexity in individual-level digital trace data, as opposed to the content traditionally examined through automated text analysis in our field. Consequently, they provide a valuable perspective for exploring the limitations of traditional approaches. We argue that recent developments within the field of Natural Language Processing, in particular, transfer learning using transformer-based models, have the potential to aid the development, application, and performance of various computational tools. These tools can contribute to the meaningful categorization of the content of social (and other) media.