A visualization with colored layers stacked on top of each other. Each layer starts at the bottom, grows in height, and fades out. Several prominent peaks stand out, and most are colored blue/promoted. A cutout diagram of one region shows closely stacked non-promoted posts, and a promoted post with a red portion before its promotion and a blue region during.

In the early years of the internet, online forums saw an influx of new accounts every September when first-year college students arrived on campus. Many of those students were using the internet for the first time, and unfamiliar with the rules and etiquette of online forums. The influx was sometimes disruptive to established online communities. In the early 1990s, as internet access became common in homes, the same thing happened at a global scale, which became known as the Eternal September. Decades later, online communities still experience large influxes of new participants, just for different reasons. CAT Lab researchers have partnered with Reddit moderators to study one potential source of disruption: the influx of new commenters from algorithmic recommendations. As part of that work, we have been developing visualizations to communicate data on algorithms and participation from different perspectives.

At the top of Reddit’s r/all page, a drop-down menu lets visitors select from options like: new, top, and hot. These recommendation algorithms use information like views, comments, and upvotes to determine which posts show up, and in what order. Posts that reach the top of the list are likely to be seen by people from all over Reddit, potentially driving up to tens of thousands of comments from people unfamiliar with the rules and etiquette of the post’s subreddit.

Statistics aren’t always good at building intuition. That’s where we turn to visualizations and data art.

To study the role of algorithmic recommendations in online communities, CAT Lab’s CivilServant platform checks and records the highest-ranked 300 posts from r/all every few minutes. We also partner with several subreddits to record the algorithmic rankings within those subreddits. Over the course of years, this becomes an incredible amount of data. We can and do use statistical tests to make scientific conclusions about that data, but statistics aren’t always good at building intuition. That’s where we turn to visualizations and data art.

Visualizing subreddit activity

The posts in a subreddit accumulate comments over time, often with active conversations occurring in multiple posts simultaneously. How does subreddit activity change when a post is promoted by a recommendation algorithm? We’ve developed the above time-series stacked area chart to convey an overall intuitive picture of the rhythm of activity on a subreddit, and the lifecycle of comment activity on each post.

In one community we are collaborating with, 0.5% of of the subreddit’s posts were promoted by the algorithm. Of those, posts reaching the top 10 received a median of 897 comments, compared to a median of 1 for non-promoted posts. And posts reaching the top 10 also drew a median of 134 comments from newcomers to the community, compared to 0 for non-promoted posts.

Title: Comment activity in promoted and non-promoted posts. A visualization with colored layers stacked on top of each other. Each layer starts at the bottom, grows in height, and fades out. Several prominent peaks stand out, and most are colored blue/promoted. A cutout diagram of one region shows closely stacked non-promoted posts, and a promoted post with a red portion before its promotion and a blue region during. Caption: Number of cumulative comments in a subreddit over previous 12 hours, grouped by post. 7 day range with a maximum of 11,833 comments. Visualization by Edward L. Platt, elplatt.com. Copyright 2025 Citizens and Technology Lab, citizensandtech.org. Creative Commons International Attribution 4.0.

In this visualization, time increases to the right. Within each time slice, each pixel corresponds to one comment. The comment counts are stacked in layers, with each layer corresponding to comments on one particular post. Non-promoted posts appear tan. Comments made while a post was promoted appear blue. Comments made before or after a post was promoted appear red. If that post was promoted by one of the algorithms, it appears green. Comment counts are cumulative over a 12 hour window. Pixels for each specific comment begin as a highly saturated color, then fade to white as time approaches the end of the 12 hour window. Using cumulative counts makes it possible to display posts with short bursts of activity, which would otherwise rise and fall too quickly to discern.

The stacked-area visualization makes it easy to see whether activity was concentrated in a single post, or spread across many posts, and whether any of those posts was listed on the front page. We find the visualization reminiscent of the strata found in sedimentary rocks, such as Michigan’s Pictured Rocks National Lakeshore, which inspired the color scheme.

We’re not the first to apply stacked-area visualizations to online communities. The History Flow project by Fernanda B. Viégas and Martin Wattenberg used similar principles to create an easy-to-interpret picture of Wikipedia edit histories. Similarly, the advantages of using stacked-area charts for complex time-series data have been explored in Lee Byron and Wattenberg’s work on Streamgraphs as well as the ThemeRivers of Susan L. Havre and collaborators.

Visualizing thread growth

We also wanted to visualize the comment activity on a single discussion thread, both before and after it is listed in the algorithmic rankings. Most online discussions, including those on Reddit, have a hierarchical structure. Everything stems, directly or indirectly, from a single post, called the root. And each comment is a reply to one other comment (or the post itself), called the comment’s parent. Computer scientists call this structure a “tree,” due to its similarity to the branches of trees. Usually, tree structures are visualized by creating a dot or “node” for each comment, and connecting each parent to its children by lines. That representation is good for showing the structure of a discussion, but doesn’t convey the growth of the discussion over time.

Title: Growth of discussion pre/post algorithmic promotion. Diagram consisting of line segments arranged like the branches of trees. Two discussion trees are shown. The base of the first tree is gray. The second tree is the same as the first, with the addition of a top portion with bushy green lines, including a very tall vertical branch. Caption: visualization by Edward L. Platt, elplatt.com. Copyright 2025 Citizens and Technology Lab, citizensandtech.org. Creative Commons International Attribution 4.0.

Instead, we tried visualizing comments not as single points, but as stems that grow over time. As those stems grow, replies branch off, forming new stems. The resulting structure is called a left-child right-sibling tree. Rather than showing the hierarchical relationship between comments and replies, it shows how the entire discussion grows over time. Branches closer to the root represent early comments, while farther branches represent later ones. Bushy areas represent times of high activity, while spindly stems represent long periods of infrequent comments. Just as living trees experience a flush of new green growth in the spring, we color parts of the discussion green when they occur after the post appeared in the algorithmic rankings. These visualizations communicate a general intuitive impression of the activity in a discussion, both before and after being listed by an algorithm.

Visualizations as analogies

The visualizations above are not the ones you would typically find in an academic publication, but they aren’t meant to be. Some visualizations provide a quick way to extract precise numerical values, while others, like these, convey a high-level impression of the data. Sometimes it’s worth giving up some precision in exchange for making data accessible to a wider audience. Visualizations can also encourage the viewer to think of the data from a certain perspective.

We encourage the viewer to think about online communities as analogous to natural systems.

By visualizing discussion threads as natural processes, we encourage the viewer to think about online communities as analogous to natural systems. That perspective raises questions. Can tools from environmental science be translated to study online communities? What does a healthy discussion look like? How does an influx of visitors from an algorithmic recommendation affect the growth and health of the discussion? How, and when, can moderators act to encourage the healthy growth of a conversation? We use visualizations like these to build intuition for a data set. To bring in perspectives and contexts. And hopefully to create images with a beauty to match the science.

These visualizations are works in progress, and we welcome feedback and questions. If you’d like to get in touch, you can contact me by email at ed@elplatt.com.