Unpacking the Social Media Phenomenon: Towards a Research Agenda
Bibliographic record
Abstract
In this paper, we highlight some of the challenges and opportunities that social media presents to researchers, and offer relevant theoretical avenues to be explored. To do this, we present a model that unpacks social media by using a honeycomb of seven functional building blocks. We then examine each of the seven building blocks and, through appropriate social and socio-technical theories, raise questions that warrant further in-depth research to advance the conceptualization of social media in public affairs research. Finally, we combine the individual research questions for each building block back into the honeycomb model to illustrate how the theories in combination provide a powerful macro-lens for research on social media dynamics.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.006 | 0.043 |
| Scholarly communication | 0.024 | 0.083 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".