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<p>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 &amp; 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.</p>

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Engagement as a predictor: regression insights from facebook activity during the Sudanese revolution

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月31日

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  • Mariam Elhussein

摘要

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.