Moderating Tamil Content on Social Media
Bibliographic record
Abstract
Tamil is a language with a long history. Spoken by over 80 million people worldwide, or over 1% of the world’s population, early inscriptions in the language date back to the 5th Century B.C.E (Murugan & Visalakshi , 2024). The language is spoken widely in India (predominantly in Tamil Nadu and Puducherry), in Sri Lanka, and across diaspora communities in Malaysia, Thailand, Canada, the United Kingdom, the United States, and beyond. Despite the widespread use of the language, there remains limited understanding of how major social media platforms moderate content in Tamil. This report examines the online experiences of Tamil users and explores the challenges of applying consistent content moderation processes for this language. This report is part of a series that examines content moderation within low-resource and indigenous languages in the Global South. **Corresponding Author: research at cdt dot org. **
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.162 | 0.674 |
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; both teacher heads agree on what is shown here.
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".