Campaigns and conflict on social media: a literature snapshot
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of this paper is to discuss the themes identified in the submissions to this volume. The findings are contextualized in recent scholarship on these themes. Design/methodology/approach The discussion is organized around predicting social media use among candidates, organizations, and citizens, then exploring differences in the content of social media postings among candidates, organizations, and citizens, and finally exploring the impact of social media use on mobilization and participatory inequality defined by gender, age, and socio-economic status. Findings This volume addresses whether social media use is more common among liberal or conservative citizens, candidates, and organizations; the level of negativity in social media discourse and the impact on attitudes; the existence of echo chambers of like-minded individuals and groups; the extent and nature of interactivity in social media; and whether social media will reinforce participation inequalities. In sum, the studies suggest that negativity and interactivity on social media are limited and mixed support for echo chambers. While social media mobilizes citizens, these citizens are those who already pre-disposed to engage in civic and political life. Originality/value This paper explores key topics in social media research drawing upon 60 recently published studies. Most of the studies are published in 2015 and 2016, providing a contemporary analysis of these topics.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it