Engagement as a predictor: regression insights from facebook activity during the Sudanese revolution
Bibliographic record
Abstract
Abstract This study examines how the Sudanese Professionals Association (SPA) strategically used Facebook to mobilize offline protests during the 2018–2019 Sudanese revolution. Contrary to narratives of leaderless digital activism, the SPA an anonymous but organized actor effectively leveraged social media to coordinate mass action under authoritarian rule. Using a dataset combining SPA Facebook posts with verified protest events from the Armed Conflict Location & Event Data Project (ACLED), the study employs hierarchical multiple regression to analyze the impact of digital activity on protest frequency and size. Key findings show that the number of posts and the day of the week significantly predicted protest incidents. In contrast, protest size was shaped by post-event engagement, specifically user responses to news posts following prior events. The analysis introduces new metrics, including a mobilization rate and a weighted response intensity score, to quantify online efforts and public sentiment. The study also identifies a strategic engagement cycle: shares peak before events, reactions and comments rise during protests, and emotional engagement continues afterward. These patterns demonstrate how digital communication was timed and structured to support offline action. The findings contribute to social movement theory and offer new tools for understanding digital mobilization in authoritarian contexts.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".