Personalizing Social Influence Strategies in a Q&A Social Network
Bibliographic record
Abstract
Research has shown that persuasive technologies are more effective when they are personalized. Persuasive strategies work differently for various people; hence a one size fits all approach may not bring about the desired change in behavior or attitude. This paper contributes to personalization in question and answer (Q&A) social networks by exploring the possibility of personalizing social influence strategies based on the computer programming skill level and the highest level of education of users. In particular, this paper explores the susceptibility of users in Stack Overflow, a Q&A social network, to social support influence strategies for novice and expert computer programmers. In addition, we explore if first degree holders respond to the social support influence strategies the same way graduate degree holders do. Using a sample size of 282 Stack Overflow users, we constructed four models using Partial Least Squares Structural Equation Modelling (PLS-SEM) and carried out multi-group analysis between these models. The results of our analysis show that social facilitation significantly influences cooperation for novice programmers, but not for expert programmers. In addition, social learning does not significantly influence the persuasiveness of the system for expert programmers compared to users who are novice in computer programming. For the users grouped according to their highest level of education, social learning influenced cooperation among the graduate degree holders and competition influenced the graduate degree holders to continue using the system. The result of this study can provide useful guidelines to social network developers that can be used in implementing personalized influence strategies in Q&A social communities.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".