Regulating Tik Tik in the global North and South: What are the Emerging Issues?
Bibliographic record
Abstract
In June 2020, shortly after a military conflict with China, India banned TikTok in the country, citing national security concerns (Murray, 2023). Compared with developing countries such as India, most of the countries that have imposed partial physical restrictions on TikTok are concentrated in the global North, including the United States, the United Kingdom, Australia, Canada, the European Union, and New Zealand (Maheshwari and Holpuch, 2023). Both the global North and South are showing differing and shifting attitudes on how to regulate TikTok. The government’s supervision of social media platforms is not limited to restricting their use on the Internet or devices. Transparency Reports released by TikTok in 2024 indicate that the number of removal requests from government departments in different regions is uneven. For example, African and South American countries do not seem to be enthusiastic about deleting TikTok posts. Why is TikTok regulated differently across these countries? In addition, Europe’s inclusion of TikTok in the Digital Markets Act (DMA) gatekeeper (Meijer and Chee, 2024) also provides another idea for supervision - using the power of the government to restrict the development of social media platforms through regular reporting and data transparency. Different countries and regions will choose different regulatory methods based on their political characteristics and actual conditions. What patterns are emerging in the regulation of TikTok? To what extent have stakeholders and policymakers’ ideas logical and consistent in their regulatory policies to achieve the desired results. This study will evaluate the emerging regulatory approaches adopted by the global North and South countries. While it is not a comparative study, the paper is a preliminary analysis of the emerging trends. patterns and rationality of policymakers in formulating regulatory policies TikTok and other Bid Techs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".