Unsupervised Framing Analysis for Social Media Discourse in Polarizing Events
Bibliographic record
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 \(\mathbb {X}\) (formerly known as 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".