The Social, Civic, and Political Uses of Instagram in Four Countries
Bibliographic record
Abstract
Instagram has more than 1 billion monthly users. Yet, little is known about how citizens engage with this platform. In this paper, we use representative survey data to examine social, civic, and political uses of Instagram by citizens in four countries: the United States, Canada, the United Kingdom, and France (n=6,291). The survey was administered to an online panel matched to the age and gender profile of each country (September to November 2019). About 40% of respondents used Instagram. This platform is especially popular among young adults (73%). Users’ network sizes are typically small, as a third of users have less than 15 followers and follow less than 15 other accounts. About 15% of users followed news organizations, a nonprofit organization or charity, or a political candidate or party. While users rarely cultivate networks with ties to these formal organizations and groups, civic and political information flows on this platform. Approximately 57% of users report seeing political information on Instagram during the previous 12 months. These findings suggest political information on Instagram flows through informal rather than formal networks. This paper establishes the importance of social, civic, and political uses of Instagram among citizens in four Western countries. Furthermore, we offer insights into the segments of the population that are intense users of Instagram, which helps to understand the role of this platform in civic and political life.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".