Bibliographic record
Abstract
We study how users trade off their needs to belong and to be different in online social networks through a field experiment that minimizes informational social influence, homophily, and identity signaling to out-group members. On a large social-networking site we find that the likelihood of adoption may in fact decrease when the option becomes more popular among a participant’s friends – a finding that contrasts sharply with previous studies of normative social influences in offline settings. Nudging users to conform by reminding them of the saliency of their choices among their friends could further decrease their likelihood to conform unless the adoption rate exceeds a certain threshold. On average, male, older, and less established, newly-registered members on the networking site are more likely to conform to their friends’ choices. We replicate the experiment in a setting that accommodates observational learning and find that the user characteristics that lead to increased nonconformity remain significant. Overall, the results suggest that there may exist important boundary conditions for behavioral convergence in online social networks and marketers may benefit from customizing their social advertising strategies based on the relative strength of normative social influence and user characteristics.
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 | 0.000 | 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 | 0.000 | 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 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".