Mobilized and Polarized: Social Media and Disinformation Narratives in the 2022 Philippine Elections
Bibliographic record
Abstract
Social media played a significant role in the 2022 Philippine national elections. Using various empirical sources, including an original preelectoral survey, we argue that social media was critical in the production, transmission, and reception of election-related information and narratives that resulted in offline and online polarization and mobilization of Filipino voters in the 2022 elections. This article discusses the role of social media in electoral politics in the Philippines relative to other factors, such as material incentives for political partisans, prior voting behavior patterns, information consumption, and long-standing grievances. We discuss how these factors inform social media’s role in mobilizing and polarizing the Philippine electorate. We also unpack the leading disinformation narratives of authoritarian nostalgia, conspiracy theory, strongman leadership, and democratic disillusionment, which fueled support for Marcos Jr. and undermined the other candidates. In conclusion, this article discusses the implications of disinformation in the 2022 elections for the post-electoral political engagement of Filipinos and its contribution to the further political dysfunction of Philippine democracy.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".