The Egyptian Revolution Goes Viral: Reading Categories of Tweets in the Twitter-created Networked Public Sphere
Bibliographic record
Abstract
The expansion of online social media (OSM) and networked information technology (NIT) use has coincided with reinvigorated democratic movements around the world, including the toppling of authoritarian governments in Tunisia and Egypt in 2011. This paper examines the variety of uses for Twitter during the Egyptian revolution, as Hosni Mubarak’s regime collapsed in less than three weeks after 30 years in power. To achieve this analysis, this paper first divided the revolution into Fisk’s four stages of political crisis. Next, the authors extracted 37,634 tweets containing key words from an archive of 16 million tweets collected from January 23-February 8, 2011. It then identified 14 categories of tweets (including Call to Action, Information Sharing, Expression of Support, and Opinion) by manually annotating a randomly selected sample of nearly 6,000 sent during the uprising. This manual annotation allowed the authors to develop category-specific patterns. After entering these patterns into a Java program, the authors ran an Automatic Content Analysis that tallied the number of tweets in each category per stage of political crisis. By correlating the Content Analysis results with the known chronology of the revolution, the results provide the answers to several questions regarding the use of Twitter during the political crisis. Throughout the revolution, Twitter was primarily used as an information-sharing tool, distributing news, updates, and critical information to protesters. As the crisis progressed, however the uses of Twitter adapted to various government policies and developments in the uprising. This examination of Twitter use can also serve as a stepping stone for other political or information scientists interested in studying the networked public sphere (NPS) and how the use of technology affects political movements.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".