Bibliographic record
Abstract
We are witnessing a changing social media environment with new actors, new influencers, and new challenges. Considering the changes on social media platforms, the rise of bots, and the increased participation of state actors, this thematic collection addresses the methodological, topical, and ethical issues of networked influence. The Facebook-Cambridge Analytica scandal opened a new chapter to analyze what “influence” means in our current, complicated social media age. As discussed in the five papers stemming from the 2018 International Conference on Social Media & Society, this special issue introduces a wide array of interdisciplinary topics and approaches that highlight the rapid changes in social media environments, use, and users—with a focus on networked influence ; by doing so, we attempt to answer some of the key research questions in this area, such as (1) how to identify and measure influence (broadly defined), (2) how to track propaganda campaigns, (3) how to effectively disseminate information and measure the public’s response to these information campaigns, (4) how do bots influence opinion trends on social media, and, finally, (5) how does the public frame privacy in a social media age?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".