Social media use and participation: a meta-analysis of current research
Bibliographic record
Abstract
Social media has skyrocketed to popularity in the past few years. The Arab Spring in 2011 as well as the 2008 and 2012 Obama campaigns have fueled interest in how social media might affect citizens’ participation in civic and political life. In response, researchers have produced 36 studies assessing the relationship between social media use and participation in civic and political life. This manuscript presents the results of a meta-analysis of research on social media use and participation. Overall, the metadata demonstrate a positive relationship between social media use and participation. More than 80% of coefficients are positive. However, questions remain about whether the relationship is causal and transformative. Only half of the coefficients were statistically significant. Studies using panel data are less likely to report positive and statistically significant coefficients between social media use and participation, compared to cross-sectional surveys. The metadata also suggest that social media use has minimal impact on participation in election campaigns.
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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 | 0.035 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.032 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".