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Record W4411672958 · doi:10.31219/osf.io/8ys9q_v1

Moderating Tamil Content on Social Media

2025· preprint· en· W4411672958 on OpenAlexaboutno aff
Mona Elswah, Aliya Bhatia

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicHate Speech and Cyberbullying Detection
Canadian institutionsnot available
Fundersnot available
KeywordsTamilContent (measure theory)Social mediaMedia contentMathematicsComputer scienceMultimediaWorld Wide WebLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

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. **

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.913
Threshold uncertainty score0.762

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.081
GPT teacher head0.274
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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