The Crux of Cross-Platform Analyses: What Can and Can’t We Do with NLP Methods?
Abstract
In communication research, Natural Language Processing (NLP) approaches have gained traction for data collection and analysis (Jünger et al., 2022). Similar to many social sciences, their use has resulted in a “computational turn” (Berry, 2011) of the field. Against the backdrop of this development, the goal of this talk is twofold. I first provide an overview of promises and pitfalls associated with NLP tools in and for social science. In particular, I discuss how the computational turn and, as a result, interdisciplinarity pose a challenge for collecting, measuring, and analyzing data in a meaningful way. I then turn to a specific problem for which NLP methods, in theory, offer great potential: cross-platform analyses. To date, most people get their news via a multitude of social media platforms. However, scholars have largely refrained from tracking communication across these in a comparative manner (Matassi and Boczkowski 2021). In a recent study (Hase et al., under review), we employed NLP methods in combination with manual approaches to address this gap. We tracked journalistic news stories (N = 4,412), including embedded images and videos (N = 6,850), across five platforms (new websites, Twitter, Facebook, Instagram, and TikTok) to answer a single question: How do news outlets select and adapt stories from their own websites for social media? Reflecting on our experiences with cross-platform data collection (e.g., combining scraping/API access with manual data collection) and data analysis (i.e., combining automated text/image classification with manual content analysis), I discuss how multi-modal and mixed-method analyses may combine the best of two worlds.
