U.S. Public Opinion and Government Regulation of Foreign Social Media Apps: A “Hidden Consensus” About What to Ban?
Bibliographic record
Abstract
How do concerns about foreign social media app’s geopolitical and economic threats and impact on the domestic political economy influence individual support for government decisions to ban such apps? We address this question through a conjoint analysis conducted in November 2024 (N =1,494). Our findings indicate that geopolitical concerns dominate the respondents’ calculations. Apps that pose low risks to U.S. military/intelligence or corporate interests, provide strong data privacy protection, or are privately owned are less likely to be targeted for bans. Political economy concerns about reciprocal access or employment opportunities are also important determinants. App-specific features mattered, but to a lesser extent. Sub-group analysis reveals a remarkable lack of heterogeneity. Our results point to a surprisingly strong “hidden consensus” about what to ban—with TikTok-like features arousing opposition among the American public—suggesting that the trajectories of the liberal international economic order and the digital world order mirror each other.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".