"Thank you for being nice": Investigating Perspectives Towards Social Feedback on Stack Overflow
Bibliographic record
Abstract
The Stack Overflow Q&A community has been frequently criticized for being a harsh, unfriendly environment. Despite numerous calls by the community to improve in this regard, prior work has shown that negative community dynamics continue to deter women, newcomers, and other marginalized groups from getting engaged. Social feedback can play a significant role in shaping community behaviour through group norm reinforcement and can, therefore, be employed as a tool to create more welcoming environments. With this in mind, in this paper we present the design and evaluation of a visible social feedback mechanism for inclusion in a Q&A platform like Stack Overflow. Through an exploratory interview study with 20 Stack Overflow members (10 men, 10 women), we explore users' perceptions of the mechanism's potential benefits and drawbacks. Our findings suggest that compared to the men in our study, the women were more open to additional social feedback on Stack Overflow, finding it a potential solution to make Stack Overflow more welcoming. Our interview findings also suggest that such a tool could be used to encourage newcomers and to allow users to show appreciation for supportive phrasing, complementing Stack Overflow's existing focus on feedback for technically accurate content.
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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 | 0.023 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".