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Title Unsupervised Framing Analysis for Social Media Discourse in Polarizing Events
Authors Hernán Sarmiento, Ricardo Córdova, Jorge Ortiz, Felipe Bravo-Marquez, Marcelo Santos, Sebastián Valenzuela
Publication date 2025
Abstract 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.
Pages article 44
Volume 19
Journal name ACM Transactions on the Web
Publisher ACM Press (New York, NY, USA)
Reference URL View reference page