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Navigating the Changing Landscape of Online Knowledge Consumption and Production in the Age of Generative AI: Evidence from Stack Overflow
Ji Eun Kim, Léa Vitale, Libby Hemphill, Yulin Yu
TL;DR
Online knowledge communities depend on users who seek information and users who produce it, but evidence is limited on how generative AI reshapes these roles and participation outcomes. This study examines Stack Overflow consumers and producers after ChatGPT’s release. Consumers shifted toward answering but received less acceptance, while producers answered less yet received greater recognition and increasingly asked about novel and emerging topics.
Problem
Evidence is limited on whether generative AI changes established participation roles in online Q&A communities and how participation outcomes vary across those roles.
Source: §1 · p. 2Method
The study examines question-asking, answer production, and participation outcomes among Stack Overflow consumers and producers following ChatGPT’s release.
Source: §6 · p. 14Results
Consumers asked fewer questions and provided more answers, whose acceptance declined; producers provided fewer answers but received greater recognition and were more likely to ask about novel and emerging topics.
Source: §6 · p. 14Takeaways & Limitations
Generative AI may enable users to participate beyond established roles without necessarily narrowing gaps in the benefits of participation.
Source: §6 · p. 14The measurement limitation constrains causal interpretation of the findings.
Source: §5.3 · p. 13
Abstract
from arXiv · showhide
Online knowledge communities rely on a division of epistemic labor between users who seek information and those who produce it. Generative AI may blur these roles, but how it reallocates knowledge-seeking and knowledge-producing activities and reshapes the nature and returns of participation remains unclear. We examine changes in question-asking and answering among Stack Overflow user groups, focusing on consumers and producers, following the release of ChatGPT. Consumers shifted from asking questions to producing answers, but their answers were less likely to be accepted relative to the pre-ChatGPT period, suggesting that increased production did not yield equal standing in the community. By contrast, producers did not change the number of questions they asked, but increasingly asked about novel and emerging topics. Although they produced fewer answers, their answers received greater recognition. These findings show that access to generative AI does not necessarily translate into equal opportunities for successful participation.
1 Introduction
Platform-level activity trends cannot show how generative AI reallocates participation across user roles. This study examines Stack Overflow users’ question-asking, answer production, and participation outcomes after ChatGPT’s release, finding role-specific changes in activity and recognition.
1 Introduction
- Platform-level engagement trends cannot reveal whether aggregate changes reflect uniform disengagement or offsetting behavioral shifts across user groups.The study examines changes following ChatGPT’s release across Stack Overflow segments with different prior participation roles.Source: §1 · p. 2
- Stack Overflow distinguishes knowledge-seeking questions from answer production and provides votes, accepted answers, and longitudinal records to study participation outcomes over time.Its programming questions are practical and comparatively externally verifiable.Source: §1 · p. 2
- Consumers asked fewer questions but provided more answers, while producers asked no more questions and provided fewer answers after ChatGPT’s release.Consumers’ answers also became more similar to AI-generated responses, consistent with AI-assisted production.Source: §1 · p. 2
- Consumers’ answers were accepted less often, while producers maintained a higher acceptance share that increased further after ChatGPT’s release.Producers were also more likely to ask about novel and rapidly emerging topics.Source: §1 · p. 3
2 Related Work
Prior work frames online knowledge communities as sustained by complementary seeking and producing roles, while generative AI may substitute for or support participation. The literature leaves open how these effects vary across user groups and whether role-crossing participants achieve comparable outcomes.
2 Related Work
- The related work treats knowledge communities as infrastructures sustained by complementary roles and reviews AI as both a substitute for and support for participation.It then turns to evidence on differences across user groups.Source: §2 · p. 3
2.1 Knowledge Consumption and Production in Online Communities
- Questions articulate information needs and create opportunities for expertise, while answers address those needs and form reusable knowledge resources.Consumers primarily ask questions, whereas producers primarily provide answers; these activities are interdependent.Source: §2.1 · p. 3
2.2 Generative AI as a Substitute for and Support for Community Participation
- Generative AI may substitute for public participation by supplying immediate answers, but it may also support users in drafting contributions and entering answer production.Private AI interactions are not scrutinized by community members or preserved in searchable archives, unlike public exchanges.Source: §2.2 · p. 4
2.3 Heterogeneous Effects of Generative AI Across User Groups
- Generative AI’s effects may vary with users’ expertise, experience, and established participation patterns, but whether role-crossing participants achieve comparable outcomes remains unresolved.Prior findings include experienced Wikipedia editors exploring new topics and newcomers facing rejected edits, as well as Stack Overflow evidence on AI adopters’ answers.Source: §2.3 · p. 5
3 Data and Methods
The study uses Stack Overflow records spanning one year before and after ChatGPT’s release, classifies users by pre-release participation, and analyzes group-week outcomes with interrupted time series. It also measures answer similarity to AI-generated responses and question novelty and complexity.
3.1 Data
- The dataset covers two years around ChatGPT’s release, with 1,806,827 questions, 2,479,528 answers, and 926,739 users active before release.Records were retrieved from the Internet Archive.Source: §3.1 · p. 5
3.2 Role Identification
- In the pre-ChatGPT period, users with fewer than 10 total items were labeled low-activity; among users with at least 10 items, those whose questions made up at least 80% were labeled consumers.Source: §3.2 · p. 5
3.3 Interrupted Time Series Analysis
- The interrupted time-series analysis models pre-release trends, the immediate release-time change, and post-release trends in group-week outcomes.Group interactions estimate differences from consumers, the reference group; the design compares observed post-event trends with a counterfactual based on historical trends.Source: §3.3 · p. 6
3.4 Similarity Between User Answers and AI-Generated Answers
- Answer similarity is measured by comparing users’ answers with GPT-3.5 answers to the same questions, with greater similarity treated as evidence consistent with AI assistance.The authors explicitly note that similarity is not direct proof that an answer was generated using ChatGPT.Source: §3.4 · pp. 6–7
3.5 Question Novelty and Complexity
- Question novelty is assessed using novel and rising tags, while programming-content difficulty is classified using a model trained on LeetCode problems.The 6,503 novel tags first appeared after ChatGPT’s release; rising tags are the top 10% by frequency growth among pre-existing tags.Source: §3.5 · p. 7
4 Results
After ChatGPT’s release, consumers shifted toward fewer questions and more answers, while producers’ question activity remained stable and their answer production declined. Participation outcomes also diverged: consumers’ answers became more AI-like but less likely to be accepted, while producers increasingly asked about novel and rising topics.
4 Results
- Consumers’ question-posting trend fell after ChatGPT (b = −0.001, p < 0.01), while producers’ trend change was near zero and not statistically significant.The combined immediate question-level change was not statistically significant.Source: §4 · p. 7
- Consumers reduced question-asking and increased answer production, while producers’ question activity remained stable and their answer production declined.Figure 1 compares weekly per-user activity against pre-event trend counterfactuals.Source: §4 · p. 7, Figure 1 · p. 8
4.2.1 Consumers produce more AI-like answers.
- Consumers’ answers became more similar to AI-generated answers after ChatGPT (b = 0.018, p < 0.001), while producers’ similarity score barely changed.The study measured semantic similarity with OpenAI’s text-embedding-3-small model.Source: §4.2.1 · p. 8, Figure 2 · p. 9
- Consumers’ accepted-answer share fell, whereas producers’ share initially rose; the acceptance gap widened as consumers’ share continued declining.The decline may reflect answer inaccuracies or askers’ preference for established producers; abandoned questions and increased competition did not appear to account for it.Source: §4.2.1 · pp. 9–10
4.2.3 Producers remain better positioned to ask questions about novel and emerging topics.
- Producers had the highest proportion of questions containing both novel and rising tags, and were more likely than other groups to ask about such topics.Pairwise proportion tests with Bonferroni correction found significant differences between all group pairs for both tag types.Source: §4.2.3 · p. 10, Table 1 · p. 10
5 Discussion
The discussion considers how generative AI reshapes participation and recognition unevenly and proposes platform designs to support frontier knowledge, contributor development, human–AI collaboration, and equitable credit. It also addresses the study’s limits: its findings document changes associated with ChatGPT’s launch but cannot establish individual AI adoption or causal effects, and are constrained by the sample and uncertainty about users’ motivations.
5.1.1 Generative AI complicates the characterization of consumers as free riders.
- Low contribution may reflect limited capacity rather than absent motivation: AI can lower barriers and make latent willingness to reciprocate observable, so user roles are not fixed types.The authors argue that roles depend partly on the technological resources shaping users’ capacity and incentives to contribute.Source: §5.1.1 · p. 11
5.1.2 Generative AI expands participation but does not yield equal benefits.
- Consumers’ increased answer production did not bring a proportional rise in accepted answers, while producers retained greater engagement with novel and emerging topics.The authors suggest that identifying valuable frontier questions may still require accumulated expertise, domain-specific judgment, and understanding the boundaries of existing knowledge.Source: §5.1.2 · pp. 11–12
5.2 Design Implications
- Platforms should support creating and validating frontier knowledge, elevating questions about unresolved or emerging topics and enabling collective sensemaking beyond efficient retrieval of known answers.The discussion points to knowledge that is not yet readily available to AI as an increasing source of value for online communities.Source: §5.2 · p. 12
- Rather than merely generating answers, AI should scaffold users’ development as knowledge producers through verification support and participation that can build expertise.Suggested support includes locating documentation, checking code, identifying unsupported claims, and comparing answers with existing knowledge.Source: §5.2 · p. 12
- Platforms could route stable, well-documented questions to AI or archives and novel, contested, or experience-dependent questions to communities, which validate emerging knowledge for future users and AI.This division of epistemic labor treats AI, existing community knowledge, and new community discussion as complementary resources.Source: §5.2 · pp. 12–13
- Credit systems should recognize contributions beyond AI-generated knowledge, including firsthand experience, verification, corrections, synthesis, and discovery of novel problems.The discussion recommends rewarding these contributions rather than concentrating credit solely on the final accepted answer.Source: §5.2 · p. 13
5.3 Limitations and Future Work
- The study could not track reputation scores or upvotes over time because the dump contained cumulative values, so it used answer acceptance as a measure of recognition.Source: §5.3 · p. 13
- Because it is extremely difficult to determine whether authors used ChatGPT, the study measures similarity between GPT-3.5-generated answers and user posts; textual overlap is suggestive but an imperfect proxy, not direct individual adoption estimates.Source: §5.3 · p. 13
- Because AI adopters cannot be reliably distinguished from non-adopters, the analyses identify changes associated with ChatGPT’s launch, not AI’s causal effect.The platform’s AI-content ban was introduced during the same period and may also have affected participation, moderation, and answer posting; future studies could use precise adoption measures, staggered policies, or exogenous access variation.Source: §5.3 · p. 13
- The sample includes users with sufficient pre-ChatGPT activity, excluding lurkers and post-release joiners and limiting generalizability to newer or less active users and other communities.It is consequently more representative of established, relatively active users than of the platform’s entire population.Source: §5.3 · p. 13
- Classifying users by prior activity captures platform roles, but responses may also vary by gender, geography, tenure, and other characteristics [1].Future work could examine these intersections while attending to privacy and statistical power.Source: §5.3 · pp. 13–14
- The observational analysis cannot explain users’ motivations; declines in answering may reflect dissatisfaction with platform policies or reduced perceived value of contributing.Interviews, surveys, or analyses of user discussions could clarify how perceptions of generative AI shape decisions to ask, answer, or leave the platform.Source: §5.3 · p. 14
6 Conclusion
The conclusion examines how ChatGPT’s release was associated with role-based changes in Stack Overflow participation. Consumers provided more answers while producers provided fewer, but producers retained greater answer recognition and were more likely to ask about emerging topics.
6 Conclusion
- The analysis uses a role-based framework to examine knowledge seeking and production among consumers, producers, hybrid users, and low-activity users on Stack Overflow.It focuses particularly on differences between consumers and producers following ChatGPT’s release.Source: §6 · p. 14
- Consumers asked fewer questions and provided more answers, whereas producers provided fewer answers after ChatGPT’s release.The paper describes this as a reversal of traditional participation patterns.Source: §6 · p. 14
- Consumers’ increased answer production was accompanied by greater similarity to AI-generated responses, consistent with generative AI lowering barriers to contribution.The conclusion presents this as consistent with AI assistance, not as proof that AI caused the change.Source: §6 · p. 14
- Consumers’ answers were accepted less often, whereas producers received greater recognition for their answers.The conclusion says the role shifts did not produce comparable outcomes across groups.Source: §6 · p. 14
- Producers remained more likely to ask questions about emerging and novel topics, including topics at the knowledge frontier.The conclusion suggests users may participate beyond established roles without necessarily narrowing gaps in participation benefits.Source: §6 · p. 14
A Sensitivity Analysis
The sensitivity analysis finds substantively consistent results across four role-classification specifications. It retains thresholds of at least 10 actions and an 80% role-assignment threshold because stricter activity thresholds leave too few users for reliable role comparisons.
A Sensitivity Analysis
- The primary specification requires at least 10 actions and assigns a consumer or producer role when at least 80% of a user’s actions match that activity.These thresholds prioritize sufficient activity and a clearly dominant behavioral pattern.Source: A Sensitivity Analysis · pp. 16–17
- Lower activity and role-assignment thresholds include more users but may make classifications less stable or blur distinctions between consumer, producer, and hybrid roles.The threshold choice trades sample size against classification precision.Source: A Sensitivity Analysis · p. 17
- Regardless of role-assignment proportion, 910,715 users (98.3% of the sample) were classified as low-activity, leaving consumer, producer, and hybrid groups extremely small and limiting reliable comparisons across roles.Thresholds above 10 actions provide little analytical benefit.Source: A Sensitivity Analysis · p. 17
- Results remained substantively consistent across all four tested specifications, indicating that the conclusions were not sensitive to the chosen thresholds.The four specifications varied the minimum-activity threshold and role-assignment threshold separately and jointly.Source: A Sensitivity Analysis · p. 17
B Examples of Stack Overflow Answers with High User–AI Similarity
The appendix presents five Stack Overflow answers with the highest semantic similarity to AI-generated answers.
B Examples of Stack Overflow Answers with High User–AI Similarity
- The section presents five Stack Overflow answers selected for having the highest semantic similarity to AI-generated answers.The supplied passage states the selection criterion but does not describe the answers themselves.Source: B Examples of Stack Overflow Answers… · p. 17
B.1 Example 1: User–AI Similarity = 0.937
The example initializes a recipient collection and fills it with the owner or administrators when either group has items. If neither does, the collection remains empty.
B.1 Example 1: User–AI Similarity = 0.937
- When either $owner or $admins is nonempty, the code populates $sentTo using push.The two mailing configuration checks and the nonempty condition precede that population step.Source: §B.1 · pp. 18–19
- The example checks whether $sentTo is nonempty before proceeding to the subsequent action.The supplied code comments describe that action only as something to do when the collection is not empty.Source: §B.1 · p. 19
- The example initializes $sentTo as an empty collection, which ensures it remains empty if both recipient conditions are false.The prose states that the collection is populated only if either $owner or $admins contains items.Source: §B.1 · pp. 18–19
B.2 Example 2: User–AI Similarity = 0.918
The examples illustrate several coding answers: filtering data to find a maximum, ordering a priority queue as a max heap, interpreting a Windows message, and formatting a Fibonacci sequence.
B.2 Example 2: User–AI Similarity = 0.918
- The pandas example filters rows to chicken and selects the row with the maximum price.The result is the row with the highest price for the chicken meat type.Source: §B.2 · pp. 19–20
B.3 Example 3: User–AI Similarity = 0.904
- The PriorityQueue comparator orders elements by their map values, making the queue act as a max heap.The element with the highest map value receives the highest priority.Source: §B.3 · p. 20
B.4 Example 4: User–AI Similarity = 0.904
- In WM_NCCREATE, “NC” means “non-client,” referring to the window area containing elements such as the title bar and border.The WM_NCCREATE message is sent before WM_CREATE when a window is first created.Source: §B.4 · p. 21
B.5 Example 5: User–AI Similarity = 0.897
- The Fibonacci-string example appends commas only when the current iteration is not the last (checked by comparing sequence with num - 1), so no extra comma appears at the end.For non-positive input it returns ''; input 9 should print '0,1,1,2,3,5,8,13,21'.Source: §B.5 · pp. 21–23
C Potential Mechanisms Behind the Decline in Acceptance of Consumers’ Answers
The follow-up analyses examine whether consumers’ declining answer acceptance reflects harder questions, unresolved answers, or greater competition. The reported patterns do not support these explanations as consumer-specific mechanisms.
Question difficulty
- Both groups increasingly targeted difficult questions, so this shift was not unique to consumers.Consumers had no significant immediate level change but a small positive trend change; producers had a significant immediate increase and a small positive trend change.Source: C Potential Mechanisms Behind the… · p. 23
Unaccepted answers
- Neither group showed a significant post-ChatGPT level or trend change in the share of answered questions without an accepted answer.The authors conclude that this outcome does not explain the consumer-specific decline in accepted answers.Source: C Potential Mechanisms Behind the… · p. 23
Answer competition
- The average number of competing answers dropped immediately after ChatGPT’s release for both groups, contrary to the increased-competition explanation.The paper measures competition as answers already posted when each response was submitted.Source: C Potential Mechanisms Behind the… · p. 23
D Analysis Results for All Four User Groups
This section reports analyses of consumers, producers, hybrid users, and low-activity users, using consumers as the reference group. The supplied table captions identify analyses of activity volume, user–AI similarity, and answer acceptance.
D Analysis Results for All Four User Groups
- The analysis covers consumers, producers, hybrid users, and low-activity users, with consumers as the reference category.The section introduces results for activity volume, user–AI similarity, and accepted-answer share.Source: D Analysis Results for All Four User… · p. 23
- Figure 4 separates consumer and producer weekly trends for answers targeting hard questions and answered questions lacking an accepted answer.Its solid lines show fitted ITS trends and dashed lines extrapolate pre-event trends as counterfactuals; annotations distinguish level and slope changes.Source: Figure 4 · p. 24
- Figure 5 tracks weekly competing-answer counts for consumers and producers, with fitted ITS trends and extrapolated pre-event counterfactuals.The annotations report immediate level changes and changes in weekly slope after ChatGPT’s introduction.Source: Figure 5 · p. 24
- Table 3 reports ITS results for questions and answers per user, while Tables 4 and 5 cover user–AI similarity and accepted-answer share.The captions identify consumers as the reference group for Tables 3–5; Table 4 uses two embedding models.Source: Table 3 · p. 25, Table 4 · p. 26, Table 5 · p. 27