Flying chairs, heated takes
Bibliographic record
Abstract
The 2024 São Paulo mayoral election sparked intense political discourse, particularly following a highly publicized altercation during a live debate on September 15. The incident, in which candidate José Luiz Datena struck Pablo Marçal with a chair, led to widespread discussion on social media, particularly on YouTube. This study investigates the dynamics of online discourse surrounding this event, focusing on audience engagement and sentiment across five major YouTube news channels: UOL, Folha de São Paulo, CNN Brasil, Poder360, and Itatiaia. Using a discourse analysis approach adapted from Teixeira et al. (2018), we collected and categorized 500 top-ranking YouTube comments, classifying them into four primary categories: Humor, Support, Criticism and Protest, and Neutral. A second layer of analysis further refined support and criticism, differentiating between pro-Datena, pro-Marçal, and general political dissatisfaction. Our findings reveal that humor was the dominant response across all platforms, suggesting a tendency toward memefication and satire in Brazilian digital political discourse. However, significant polarization was observed, with Datena receiving both overwhelming support and the highest level of criticism across outlets. Media framing influenced audience reactions, as outlets with in-depth coverage fostered broader critiques, while those with shorter, sensationalist clips amplified polarized sentiments. This study contributes to research on political communication and social media discourse by demonstrating how digital platforms mediate political controversies and shape public perception. The results highlight the role of algorithmic content curation in reinforcing ideological divides and fostering emotionally charged interactions. By offering a systematic analysis of audience reactions, this study provides insights into the evolving nature of digital political engagement in Brazil and lays the groundwork for future research on media framing and discourse analysis in online environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.066 | 0.010 |
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".