### Slide 1 – Title

**Title:** Did Stack Overflow Answers Increase After ChatGPT?
- Changes in Stack Overflow answer activity post-ChatGPT launch
- Impact of related policy events
- Developer behavior balancing Stack Overflow vs. AI tools

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### Slide 2 – Research Question

**Research Questions:**
1. Volume of answers:
   - Did Stack Overflow answers change systematically after ChatGPT launched (late 2022)?
2. Policy/event impact:
   - Did AI-answer policies and moderation events create additional shifts?
3. Substitution effect:
   - Are heavy ChatGPT users visiting/answering less on Stack Overflow?

**Approach:**
- Look for structural breaks in answer time series
- Link site-level patterns to developer survey data

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### Slide 3 – Data Sources

**Dataset 1:**
- Monthly new answer counts (2018–2025)
- Pulled from Stack Exchange Data Explorer
- Includes deleted posts
- Provides pre-ChatGPT baseline and post-event window

**Dataset 2:**
- Microdata from Stack Overflow Developer Surveys (2023–2025)
- Focus:
  - Visit frequency
  - Adoption of AI tools like ChatGPT

**Exploratory Plots:**
- Raw time series
- Pre/post comparisons
- Seasonality
- Moving averages

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### Slide 4 – Preliminary Patterns

**Key Observations:**
- Long-run time series:
  - Downward drift in answers pre-2022
  - Sharper drop in level and slope post-ChatGPT launch
- Pre/post comparison:
  - Post-ChatGPT period sits lower, even after accounting for seasonal dips (e.g., summer, year-end)
- Seasonal plots:
  - 2018–2025 share consistent within-year rhythm
  - Confirms changes aren’t due to seasonality

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### Slide 5 – Methodology

**Modelling Strategies:**
1. **Interrupted Time-Series Regression (ITS):**
   - Predictors: time trend, level jump (ChatGPT launch), slope change
   - Optional indicators: policy/moderation periods
2. **Poisson/Negative-Binomial Count Models:**
   - Predictors: same as ITS
   - Suitable for count data
   - Quantifies percentage changes per month
3. **ARIMA Model:**
   - Trained on pre-ChatGPT data
   - Forecasts counterfactual trajectory
   - Compares observed vs. predicted post-event counts
4. **Survey Logistic Regression:**
   - Predicts frequent Stack Overflow visits
   - Predictors: ChatGPT usage, demographics

**Diagnostics:**
- Residual checks
- Over-dispersion
- Out-of-sample performance

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### Slide 6 – Model Fits & Counterfactuals

**Findings:**
- **Interrupted Time-Series Regression:**
  - Downward level shift post-2022
  - Steeper negative slope post-ChatGPT
  - Controls for pre-existing trend
- **Poisson Model:**
  - Pre-ChatGPT: mild monthly contraction
  - Post-ChatGPT: steeper decline (compounds over time)
- **ARIMA Forecast:**
  - Trained on pre-ChatGPT data
  - Post-2022 counts fall below 80% prediction interval
  - Observed counts never recover

**Takeaway:**
- Structural break in answer supply post-ChatGPT and policy changes
- Changes not explained by trend/seasonality alone

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### Slide 7 – Survey Results

**Key Insights:**
- **ChatGPT Adoption (2023):**
  - Widespread among developers, especially heavy coders
  - Daily use common in workflows
- **Visit Frequency (2023–2024):**
  - 2023: Heavy ChatGPT users visit Stack Overflow at similar daily rates as non-users
  - 2024: Frequent visits drop more for heavy ChatGPT users
- **Logistic Regression:**
  - ChatGPT usage alone: weak predictor of visit frequency (low-50% accuracy)
  - Combined with cross-tabs: supports partial substitution (marginal questions shifted to ChatGPT)

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### Slide 8 – Key Findings

**Summary:**
- Monthly answers on Stack Overflow:
  - Sharp drop post-ChatGPT release
  - Continued lower trend (even after controlling for pre-existing decline)
- Policy/moderation events:
  - Additional dips align with governance decisions
  - Suggest amplification of ChatGPT effect
- ARIMA counterfactuals:
  - Post-2022 counts outside expected range of pre-ChatGPT dynamics
- Substitution effect:
  - Heavy ChatGPT users less likely to visit Stack Overflow daily over time

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### Slide 9 – Limitations

**Caveats:**
1. **Causality:**
   - Overlap of ChatGPT, AI policies, moderation strike
   - Broader economic/tooling trends also in play
2. **SEDE Data:**
   - Doesn’t capture moderation queues/private spaces
   - Some activity may be invisible
3. **Survey Data:**
   - Self-reported
   - May under-represent active answerers or certain regions/roles

**Interpretation:**
- Results are **correlational evidence** of shifts in answer supply/usage patterns
- Not a precise causal estimate of “ChatGPT effect”

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### Slide 10 – Implications & Future Work

**Implications:**
- Answer supply sensitive to:
  - Assistance tooling
  - Governance decisions
- Platforms should:
  - Carefully consider AI policies/moderation capacity
  - Explore integration with conversational assistants (e.g., structured answer APIs)

**Future Work:**
- Tag-level/user-cohort analyses
- Stronger quasi-experimental designs (e.g., synthetic controls)
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