Stack Overflow Post-Mortem

This is my final report for CSC 369: Introduction to Distributed Computing at Cal Poly.

Introduction

Now that Stack Overflow (SO, or just “Stack”) is dead, it is time to perform an autopsy. Stack was infamous for being an impenetrable forum, filled with–to keep it PG–obnoxious people who were often technically correct and reveled in proving it. Constantly. So how did Stack’s culture and community affect the people who tried to wade into it?

When it was in its prime, SO was a genuinely invaluable resource. Personally, I’ve found plenty of ultra-specific solutions to weird problems (often the blame lands squarely at the feet of the godforsaken OS formerly known as Windows) on Stack Overflow in its heyday, but I never felt the need to join the ranks and ask questions or submit answers. That hesitation, accelerated by LLMs, is what killed Stack. Learning what not to do from Stack can help if we ever want, or have, to design another technical or education-focused forum (Moltbook doesn’t count).

Research Question

We have a few main questions: what features predict retention for first-time Stack Overflow users (a), and further what predicts whether they will answer questions (b). Do users fall into distinct activity patterns as well, such as bursty or regular posting, more Stack usage on weekdays versus weekends, etc. Lastly, do (a) and (b) have any relation to the activity behaviour?

Data Dictionary

Data dictionary sourced mostly from:

1. ClickHouse Stack Overflow dataset documentation: https://clickhouse.com/docs/getting-started/example-datasets/stackoverflow⁠
2. Stack Exchange public schema reference: https://meta.stackexchange.com/questions/2677/database-schema-documentation-for-the-public-data-dump-and-sede⁠

What follows are brief descriptions of the features we used in our (regularized) models.

• First post year: calendar year of a user’s first post.
• First post is a question: 1 if the first post is a question, otherwise 0.
• Log of posts in first 30 days: log1p transform of month-1 post count.
• Log of active days in first 30 days: log1p transform of month-1 active-day count.
• Question share in first 30 days: month-1 question count divided by month-1 post count.
• Answer share in first 30 days: month-1 answer count divided by month-1 post count.
• Posts per active day in first 30 days: month-1 post count divided by month-1 active-day count.
• Mean post score in first 30 days: average score of month-1 posts.
• Mean body length in first 30 days: average character length of month-1 post bodies.
• Weekend share in first 30 days: share of month-1 posts created on Saturday or Sunday.
• Retention within 1 year: 1 if user posts again after day 30 and within one year.
• Becomes Answerer within 1 year: 1 if user posts at least one answer after day 30 and within one year.

Descriptive Results

Coverage of modeled users

As we can clearly see, the data are quite large, with around 6 million unique users. Our data spans 2008 to early 2024 and is sourced from ClickHouse. Mostly for reference, we can see that only about a third of users stuck around after a year, and fewer than one in five users ever answered a question within a year of creating their account.

The rise and fall of Stack Overflow is an archetypal hero’s journey, with a somewhat long climax from 2014 to 2016 and a brief revival during the pandemic in 2020. The death knell was, unsurprisingly, heralded by the release of ChatGPT in late 2022, though it was really just a finishing blow to a dying platform. Though the purpose of this report is not to elucidate what led to the platform’s downfall, it is an interesting question, and these data probably support such an endeavor, though we leave that for further study.

Monthly new users are another way of telling the same story as the previous plot, with a sharp rise in the early 2010s, a bit of a plateau, followed by a freefall. We can see that monthly new users at the tail end of 2024 seem to have returned to levels similar to those of a decade ago.

Looking for any signs of seasonality, we can see that the median number of posts per week tends to decrease throughout the year, with dips around New Year’s, when most companies are off. Here we filter out 2008 and 2024 because we do not have data for those full years.

Note that the next few plots about yearly trends are restricted to data from weeks 2 to 51, inclusive.

Note the different scale on the y-axis compared to the previous plot. Median posts per week seem to be somewhat variable, but most interestingly, there appear to be fewer posts in the second half of the year than in the first–perhaps this is due to decaying motivation, rejected New Year’s resolutions, seasonal depression, or general calendar-inspired nihilism.

Splitting these out by year and making the comparison relative to each year’s median, we can see the downtick in the second half of the year for some years, though others show the opposite pattern. Of course, at this point we are basically just looking at the growth of Stack Overflow as a platform, with some years showing growth (an increasing number of weekly posts relative to the median) and recent years showing market loss.

If we look at raw counts, this picture is a bit clearer. There still does appear to be a slight seasonal effect, as H2 has fewer posts per week (every line seems to be broadly decreasing), but obviously the stronger signal is the year itself; that is, between-group differences seem to be more obvious than within-group differences.

The y-axis is the number of posts and answers made on that day across all years; here, we are not dropping data from 2008 and 2024 since we are at the week level. At the intra-week level we find, unsurprisingly, that the total number of posts and answers drops markedly, basically being cut in half over the weekends. The ratio of questions to answers seems to be fairly constant across the weekdays too.

Predictive Models

Model performance

The two models are regularized, so we elided manual feature selection and allowed the models to decide for themselves. We used one linear and one nonlinear model: a logistic regression with elastic-net regularization and a random forest. We skipped CV for the random forest due to computational constraints. The test ROC-AUC values are similar for both models, sitting at around 0.65 for retention and 0.7 for Becomes Answerer–both nominally better than the guessing rate of 0.5, but not resoundingly so.

We fit two models using rolling five-fold cross-validation and a test set with minimal tuning (larger and more thorough grids would be an easy future extension). Specifically, we tuned the elastic-net model’s penalty parameter and elastic-net balance over two small grids (penalty: {0.03, 0.1, 0.3, 1.0}, ratio: {0.1, 0.3, 0.6, 0.9}). The random forest was tuned more lightly, with 300 trees and sqrt(p) candidate features at each split.

To avoid data leakage, the Becomes Answerer models exclude first-month Question Share and Answer Share from the predictor set. Including either would partially encode posting-type behavior too directly and would artificially inflate both the reported model performance and the apparent feature importance rankings.

Retention Model Results

Elastic Net retention feature importance

Finally, reaching the crux of our analysis, we can look at which features these models considered most important for predicting retention. First, we can see that both the linear and nonlinear models found active days and number of posts in the first month to be very important–for both models, far more important than the other features. From the logistic model, we can see that these features are positively correlated with retention.

After those, though, the models differ on which features are deemed important–though perhaps a case could be made for first post year being weighted similarly by both models. The logistic model ascribes a negative correlation between first post year and retention.

Interestingly, the nonlinear model finds mean post length in the first month to be highly important, while the logistic model mostly dismisses it. That the two models disagree on some variables is not surprising (the random forest can capture nonlinear relationships between the features, unlike the logistic regression), but it is interesting nevertheless.

Becomes Answerer Model Results

Looking instead at conversion rates, we can see that most of the same features are deemed important, though to different degrees, across both models. After removing Question Share and Answer Share to avoid leakage, first-month active days and posts still appear near the top of both importance rankings, suggesting that sustained early engagement remains the clearest signal for later conversion into answering behavior.

Conclusions

It seems like active days and number of posts in the first month, along with joining year–and maybe mean post length from the first month–predict retention for first-time Stack Overflow users. First-month active days and number of posts–and perhaps, again, mean body length–seem to be the most important factors for predicting conversion from Stack questioner to answerer.

What these models seem to tell us, then, is that the first month has a disproportionate effect on a user’s Stack Overflow experience. This aligns with common ideas and beliefs around onboarding and reducing friction for first-time users. In an environment like Stack Overflow, it seems important to ensure that users check frequently and feel comfortable asking questions. Since SO is a forum where different users may frequent essentially different portions of the site, further analysis should examine the tags of a post.

Further analysis could also incorporate the sentiment and types of feedback users get from existing users on the site–we make the obvious claim that clear, helpful, and positive answers would boost retention and brighten a user’s view of Stack Overflow. Of course, Stack is or was infamous for having plenty of querulous pedants, and we are sure that its ill-gotten reputation alone must have driven users away from the site, though we cannot easily test that hypothesis with these data.

Future work could be on improving the retention and conversion models, and training on larger subsets of the data. A time series-native approach may prove fruitful as well, as well as predicting number of answers instead of our binary approach.