Mapping Government Use of Social Media Influencers for Policy Promotion
Bibliographic record
Abstract
This study explores how national governments leverage social media through influencer partnerships and digital campaigns to promote cultural values and policy goals. Covering a broad spectrum of governmental bodies (e.g., ministries and officials), the research highlights the variety of influencer–government partnerships and collaborations. The study comes at a time when diverse regulatory frameworks are emerging globally to govern influencers’ activity, mandating transparency in sponsorships, protecting consumer interests, and setting boundaries on influencer involvement in governmental and political campaigns. The methodology combines two main steps: (a) a web search of news articles and blogs to identify relevant examples of government–influencer collaborations; (b) a manual annotation of government-led influencer strategies of the retrieved examples based on thematic areas, degree of autonomy in the partnership, and narrative strategy. The study focuses on France, the US, and Canada, chosen for their advanced digital environments and initiative-taking approaches in both social media regulation and public diplomacy. The main contribution of the study is to develop a typology of government–influencer collaborations to align public perception with (inter)national policy goals and reach their target audiences.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".