Unsupervised Framing Analysis for Social Media Discourse in Polarizing Events

This study investigates the concept of frames in the realm of online polarization, with a focus on social media platforms. The research extends the understanding of how frames—emerging, complex, and often subtle concepts—become prominent in online conversations that are polarized. The study proposes a comprehensive methodology for identifying and characterizing these frames, integrating machine learning techniques, network analysis algorithms, and natural language processing tools. This method aims for generalizability across multiple platforms and types of user engagement. Two novel metrics, homogeneity and relevancy are introduced for the rigorous evaluation of identified frame candidates.

Grounded in several foundational presumptions, including the role of topics and multi-word expressions in framing, the study sheds light on how frames emerge and gain significance within digital communities. The research questions explored include the methods for identifying frames, the variability and significance of these frames, and the effectiveness of different computational techniques in this context.

To validate the approach, we present a case study of the 2021 Chilean presidential election, using data from both Twitter and WhatsApp platforms. This real-world application allows for the examination of how frames fluctuate in response to events and the specific mechanisms of platforms. Overall, the study makes several key contributions to the field, offering new insights and methodologies for analyzing the complexities of online polarization. It serves as groundwork for future research on the dynamics of online communities, especially those associated with distinctly polarized events.