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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".