Political fact-checking in the Middle East: What news can be verified in the Arab world?
Bibliographic record
Abstract
Abstract This study examines the news selection processes followed by fact-checking organizations in the Middle East, specifically Egypt, Jordan, and the United Arab Emirates, and gatekeeping such organizations face while working under authoritarian rule. By reviewing fact-checked news posted on the Facebook pages of six Arabic language organizations: Da Begad, HereszTruth, Fatabyyano, Matsad2sh, MisbarFC, and Saheeh Masr, this study manually analyzes about 5,000 fact-checked news stories to understand the extent of political fact-checking performed on Arab presidents, heads of government, and rulers, along with the most verified news topics. Results show that organizations in the Middle East rarely fact-check Arab rulers or refute their claims, while their news selection process prioritizes human interest topics. The study suggests that Arab fact-checkers resort to self-censorship due to gatekeeping influences that impact the region’s media climate.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".