Towards Understanding Users' Motivation in a Q&A Social Network Using Social Influence and the Moderation by Culture
Bibliographic record
Abstract
Active participation of users in Q&A social networks like Stack Overflow is key to the sustenance of the network. One way to encourage participation is to allow collaboration or cooperation between users in order to improve question and answer posts, and allow users to learn from one another. In order to implement strategies that encourage cooperation, it is important to understand what influences the users in the network to cooperate. In this extended abstract, we investigate the social support principles that influence cooperation in Stack Overflow. Using a sample size of 282 Stack Overflow users, we develop and test a global research model using partial least squares structural equation modelling (PLS-SEM). We further investigate any possible differences in the effect of these strategies between cultures, by testing two cultural subgroups; collectivist and individualist cultures. Our results show that social learning significantly influences cooperation in Stack Overflow at the global level. However, at the cultural subgroup level, recognition influences cooperation among collectivists, while social facilitation influences individualists to cooperate. These findings suggest possible design guidelines in the development of successful personalized Q&A social networking sites that encourage participation through cooperation.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".