This post is about my (Nathan TeBlunthuis) paper (pdf) just published at ICWSM 2025.

Often, several different online communities exist where similar people talk about similar things. This is really easy to observe from browsing platforms like Reddit or Facebook groups.

For example, as we can see from this visualization of clustered subreddits with overlapping users, there are many different subreddits related to cycling. We see some communities have different emphases in complementary ways like “fixedgearbicycle” and “bicycletouring” — these are different types of cycling. But why have a community for
“cycling” and a different one for “bicycling”? A number of puzzles appear when we reflect on the existence of such related communities.

How do online communities relate to each other?

Why not have one large community that does everything?

How do people construct these systems of related online communities?

I investigated these questions in my dissertation using the theoretical lens of organizational ecology drawn from organizational sociology. This new paper explored some findings from earlier projects in more depth. The paper I published in ICWSM 2022 (pdf), takes up the question of ecological relationships among online communities. I used time series models to infer networks of competition and mutualism between overlapping online communities. This work found evidence that they tended to be mutualistic. For example, the diagram below shows a network of mental health subreddits that is dense with mutualism.

However, this method, based on vector autoregression (VAR) models of activity, assumes that these relationships are static and constant over time. But dynamics of attention online are often bursty, and online communities grow, decline, and change over time in other ways. So, in this new work, I adopted nonlinear models called (regularized) S-map that can model more complex dynamics.

Since I found in the previous work that mutualism tended to happen more often than competition, I wanted to find out if that result was robust using the S-map. Since the S-map breaks these relationships down into episodes of competition or mutualism it afforded testing a more nuanced hypothesis about this tendency towards mutualism.

H1: Mutualistic interactions will be more frequent and longer lasting than competitive interactions.

In the another empirical paper previously published at CSCW 2022 (acm dl), we focused on the question of why people build overlapping online communities and found that they complementary sets of benefits to members, as illustrated below. Trade-offs between the benefits lead to specialized roles for different types of communities.

This reflects propositions from ecology that specialization can be a strategy to avoid competition. The new study seeks to provide more generalizable quantitative evidence about how online communities find their specialized niches. Ecology theory suggests that online communities, similar to organizations or organisms, might adapt to increase specialization and thereby promote more mutualistic relationships. To investigate whether people build specialized online communities through such an adaptive feedback process, I set out to test the following two hypotheses:

H2: Two communities having greater competition (mutualism) will subsequently have greater decreases (increases) in overlap.

H3: Two subreddits having decreasing (increasing) overlap will subsequently have greater mutualism (competition).

Methods and measures

To test these three hypotheses, I had to measure competition/mutualism, and overlap within clusters of related subreddits over time. I made topic- and user-overlap measures based on a community embedding via the LSA algorithm. To create the clusters I reused the approach from the earlier paper by using the HDBSCAN algorithm based on user overlap. As mentioned above I used the Regularized S-MAP algorithm to create a dynamic measure of ecological influence. With these longitudinal measures in hand I could test the hypotheses using two-way fixed-effects panel data estimators with dyad-robust standard errors. That’s a brief and dense summary of the methods. The chart below might help you make sense of it, but if you care to fully understand you’ll want to check out the full paper.

Here are a few final notes on the data and methods. The data came from the Pushshift Reddit archive of submissions and comments from December 5^th 2015 to April 13^th 2020. I Started with the 19,533 subreddits that were active during at least 20% of study period weeks, excluding NSFW subreddits. HDBSCAN clustering discovered related 1,919 clusters of 8,806 subreddits having 48,484 relationships measured 17,374,116 times over 758 weeks.

Results

I found support for H1, which predicted that mutualistic interactions will be more frequent and longer lasting than competitive interactions. The plot below shows evidence in favor of the hypothesis. First, we can see clearly that the longest episodes tend to be mutualistic.
Notably, these ecological relationships are often bursty and short-lived. The average length of a mutualistic episode was 2.13 weeks and the average length of a competitive episode was just 1.83 weeks.

I also found support for H2, which predicted that I’d find positive coefficients for previous ecological interaction indicating that competition predicts decreases in overlap. Indeed, the panel regression models found that online communities tend to increase their specialization a bit in relatively competitive conditions, by about 0.02 standard deviations in term or user overlap for every 1-unit increase in competition.

Do increasingly specialized communities tend to decrease their competition as predicted by H3? My analysis didn’t find evidence for this. In fact, according to the panel regression models, after specialization increases, competition actually tends to increase as well.

Discussion

What to take away from all this? I still think the most important finding from this work to me is the robustness of the tendency toward mutualism among online communities. Unlike firms or other organizations that demand relatively exclusive commitments from their members, it is easy to participate in many online communities. Where classical organizations (imagine firms, churches, sports teams, nonprofit, and state organizations) seem likely to compete over employees, customers, or members online communities seem to benefit to some extant from sharing users with each other. I suspect this has to do with the ease with which nonrival content, ideas, and knowledge move between communities.

A second important takeaway from this work is that I think the evidence it finds for the adaptation explanation for the tendency toward mutualism isn’t all that convincing. Sure, communities in competition tend to become more specialized, but the effect size is pretty small and the fact that specialization doesn’t reduce competition suggests that it isn’t truly adaptive in the strongest sense. Put another way, specialized online communities might be made via an adaptive process, or they might be born out of the intentions and designs of their founders and early joiners. This work finds a bit of evidence for how specialization might be made, but the born process merits more investigation.

One clue about the significance of design for specialization comes from fellow CDSC-er Jeremy Foote‘s a nice CHI paper (acm dl) last year on how the early stages of a subreddit’s development are important to its trajectory and found that most subreddit creators didn’t set out to create a large community. Another study (arxiv.org), by Chenhao Tan on “community genealogy” shows how the growth of new subreddits often seems to depend on having high overlap with a “parent” subreddit. These papers don’t focus on specialization, but it would be cool to see future work take up these ideas.

If you enjoyed reading this summary or want to learn more, please check out the full paper. I got the chance to speculate a bit about what sorts of future technology designs might assist community leaders in crafting online communities to fill ecological roles. I also got to engage with ecological theory in a new way writing this. I hope you read and enjoy.

Finally, I wasn’t able to attend ICWSM in person this year, so I want to thank Kristen Engel for presenting on my behalf. I also want to note that CDSC-er Kaylea Champion and I were both recognized as “best reviewers” at the conference.

This work started as a chapter of my dissertation. Thanks to the committee — Professors Benjamin Mako Hill, Kirsten Foot, Aaron Shaw, David McDonald and Emma Spiro.

I also gratefully acknowledge support by NSF grants IIS-1908850 and IIS-1910202 and GRFP \#2016220885. This work was facilitated through the use of the advanced computational infrastructure provided by the Hyak supercomputer system at the University of Washington and TACC at the University of Texas.

Community decay and abandonment are persistent risks to free/libre and open source software (FLOSS) projects. As such, large institutions such as GitHub or Mozilla offer advice to FLOSS projects on how to organize their work for sustainability and community-building. Guides recommend the production of README files and CONTRIBUTING guides as useful tools in recruiting new project contributors and driving activity. Yet though the development of these documents is widely-suggested, there is little empirical study of how projects use these files and what happens when documents are introduced to projects.

In one of the first empirical studies of the initial publication of documentation files, our findings suggest a disconnect between institutional recommendations and FLOSS projects’ actual use the documents. Instead of being proactively developed and community-oriented, first-version files are published following an increase of activity and focus on the functional details of using or contributing to the library.  Often, documents are published with hardly any content at all, with projects publishing empty or minimal files. We found no support for any causal claims around the nature of a document’s depth or focus and subsequent project activity. 

Our results suggest that projects may use these documents to perform a norm. The publication of empty documentation files implies that an empty file in their home directory was more important to projects than any benefits of document contents. Our results also suggest that projects may use these documents to ‘get their house in order’ after an influx of activity.

The guides and recommendations that we examined did not specify when projects should take what actions to grow sustainably. This lack of specificity limits the utility for projects trying to figure out how to sustain themselves in ever-changing environments. The work necessary to develop meticulous, community-oriented files may not be a good time investment for early-stage projects with only a handful of contributors. More research is necessary to develop useful context-situated recommendations to support FLOSS projects adaptation. 

This paper was presented a few weeks ago in Ottawa at the International Conference on Cooperative and Human Aspects of Software Engineering (CHASE) 2025. A pre-print of the paper can be found here; the data and code for the project can be found here.

This research wouldn’t be possible without the work of the volunteers producing FLOSS who have made their work available for inspection. We also gratefully acknowledge support from the Ford/Sloan Digital Infrastructure Initiative (Sloan Award 2018-113560) and the National Science Foundation (Grant IIS-2045055). This work was conducted using the Hyak supercomputer at the University of Washington as well as research computing resources at Northwestern University.

Bots made by end users are crucial to the success of online communities, helping community leaders moderate content as well as manage membership and engagement. But most folks don’t have the resources to develop custom bots and turn to existing bots shared by their peers. For example, on Discord, some especially popular bots are adopted by millions of communities. However, because these bots are ultimately third-party tools — made by neither the platform nor the community leader in question — they still come with several challenges. In particular, community leaders need to develop the right understandings about a bot’s nature, value, and use in order to adopt it into their community’s existing processes and culture.

In organizational research, these “understandings” are sometimes described as technological frames, a concept developed by Orlikowski & Gash (1994) as they studied why technologies became used in unexpected ways in organizational settings. When your technological frames are well-aligned with a tool’s design, you can imagine that it is easier to assess whether that tool will be useful and can be smoothly incorporated into your organization as intended. In the context of online communities, well-aligned frames can not only reduce the labor and time of bot adoption, but also help community leaders anticipate issues that might cause harm to the community. Our new paper looks to communities on Discord and asks: How do community leaders shift their technology frames of third-party bots and leverage them to address community needs?

Emergent social ecosystems around bot adoption

Our study interviewed 16 community leaders on Discord, walking through their experiences adopting third-party bots for their communities. These interviews underscore how community leaders have developed social ecosystems around bots: organic user-to-user networks of resources, aid, and knowledge about bots across communities.

Despite the decentralized arrangement of communities on Discord, users devised and took advantage of formal and informal opportunities to revise their understandings about bots, both supporting and constraining how bots became used. This was particularly important because third-party bots pose heightened uncertainties about their reliability and security, especially for bots used to protect the community from external threads (such as scammers). For example, interviewees laid out concerns about whether a bot developer could be trusted to keep their bot online, to respond to problems users had, and to manage sensitive information. The emergent social ecosystems helped users get recommendations from others, assess the reputation of bot developers, and consider whether the bot was a good fit for them along much more nuanced dimensions (in the case of one interviewee, the values of the bot developer mattered as well). They also created opportunities for people to directly get help in setting up bots and troubleshooting them, such as via engaged discussions with other users who had more experience.

Our findings underscore a couple of core reasons why we should care about these social ecosystems:

1. Closing gaps in bot-related skills and knowledge. Across interviews, we saw patterns of people leveraging the resources and aid in social ecosystems to move towards using more powerful but complex bots. Ultimately, people with diverse technical backgrounds (including those who stated they had no technical background) were able to adopt and use bots — even bots involving code-like configurations in markdown languages that might normally pose barriers. We suggest that the diffusion of end-user tools on social platforms be matched with efforts to provide bottom-up social scaffoldings that support exploration, learning, and user discussion of those tools.
2. Changing perceptions of the labor involved in bot adoption. The process of bot adoption as a deeply social one appeared to impact how people saw the labor they invested into it, shifting it into something fun and satisfying. Bot adoption was both collaborative, involving many individuals as a user discovered, evaluated, set up, and fine-tuned bots; and communal, with community members themselves taking part in some of these steps. We suggest that bot adoption can provide one avenue to deepen community engagement by creating new ways of participating and generating meta discussions about the community, as well as the platform.
3. Shaping the assumptions around third-party tools. Social ecosystems enabled people to cherry-pick functions across bots, enabling creative wiggle room in curating a set of preferred functions. At the same time, people were constrained by social signals about what bots are and can do, why certain bots are worth adopting, and how the bot is used. For example, people often talked about genres of bots even though no such formal categories existed. We suggest that spaces where leaders from different communities interact with one another to discuss strategies and experiences can be impactful settings for further research, intervention, and design ideas.

Ultimately, the social nature of adopting third-party bots in our interviews offers insight into how we can better support the adoption of valuable user-facing tools across online communities. As online harms become more and more technically sophisticated (e.g., the recent rise of AI-generated disinformation), user-made bots that quickly respond to emerging issues will play an important role in managing communities — and will be even more valuable if they can be shared across communities. Further attention to the dynamics that enable tools to be used across communities with diverse norms and goals will be important as the risks that communities face, and the tools available to them, evolve.

Engage with us!

If you have thoughts, ideas, questions, we are always happy to talk – especially if you think there are community-facing resources we can develop from this work. There are a few ways to engage with us:

• Drop a comment below this post!
• Check out the full paper, available ✨ open access ✨ in the ACM Digital Library.
• Come by the talks we’ll be giving:
  • at ICA2024 on Saturday, June 22, 2024 in the “Digital Networks, Platforms, and Organizing” session at 3:00-4:15PM in Coolangatta 4 (Star L3);
  • at CSCW2024 in November; schedule is still forthcoming!
• Connect with us on social media or via email.

Hundreds of new subreddits are created every day, but most of them go nowhere, and never receive more than a few posts or comments. On the other hand, some become wildly popular. If we want to figure out what helps some things to get attention, then looking at new and small online communities is a great place to start. Indeed, the whole focus of my dissertation was trying to understand who started new communities, and why. So, I was super excited when Sanjay Kairam at Reddit told me that Reddit was interested in studying founders of new subreddits!

The research that Sanjay and I (but mostly Sanjay!) did was accepted at CHI 2024, a leading conference for human-computer interaction research. The goal of the research is to understand 1) founders’ motivations for starting new subreddits, 2) founders’ goals for their communities, 3) founders’ plans for making their community successful, and 4) how all of these relate to what happens to a community in the first month of its existence. To figure this out, we surveyed nearly 1,000 redditors one week after they created a new subreddit.

Lots of Motivations and Goals

So, what did we learn? First, that founders have diverse motivations, but the most common is interest in the topic. As shown in the figure above, most founders reported being motivated by topic engagement, information exchange, and connecting with others, while self-promotion was much more rare.

When we asked about their goals for the community, founders were split, and each of the options we gave was ranked as a top goal by a good chunk of participants. While there is some nuance between the different versions of success, we grouped them into “quantity-oriented” and “quality-oriented”, and looked at how motivations related to goals. Somewhat unsurprisingly, folks interested in self-promotion had quantity-oriented goals, while those interested in exchanging information were more focused on quality.

Diversity in plans

We then asked founders about what plans they had for building their community, based on recommendations from the online community literature, such as raising awareness, welcoming newcomers, encouraging contributions, and regulating bad behavior. Surprisingly, for each activity, about half of people said they planned to engage in doing that thing.

Early Community Outcomes

So, how do these motivations, goals, and plans relate to community outcomes? We looked at the first 28 days of each founded subreddit, and counted the number of visitors, number of contributors, and number of subscribers. We then ran regression analyses analyzing how well each aspect of motivations, goals, and plans predicted each outcome. High-level results and regression tables are shown below. For each row, when β is positive, that means that the given feature has a positive relationship with the given outcome. The exponentiated rate ratio (RR) column provides a point estimate of the effect size. For example, Self-Promotion has an RR of 1.32, meaning that if a given person’s self-promotion motivation was one unit higher the model predicts that their community would receive 32% more visitors.

A number of motivations predicted each of the outcomes we measured. The only consistently positive predictor was topical interest. Those who started a community because of interest in a topic had more visitors, more contributors, and more subscribers than others. Interestingly, those motivated by self-promotion had more visitors, but fewer contributors and subscribers.

Goals had a less pronounced relationship with outcomes. Those with quality-oriented goals had more contributors but fewer visitors than those with quantity-oriented goals. There was no significant difference in subscribers for founders with different types of goals.

Finally, raising awareness was the strategy most associated with our success metrics, predicting all three of them. Surprisingly, encouraging contributions was associated with more contributors, but fewer visitors. While we don’t know the mechanism for sure, asking for contributions seems to provide a barrier that discourages newcomers from taking interest in a community.

So what?

We think that there are some key takeaways for platform designers and those starting new communities. Sanjay outlined many of them on the Reddit engineering blog, but I’ll recap a few.

First, topical knowledge and passion is important. This isn’t a causal study, so we don’t know the mechanisms for sure, but people who are passionate about a topic may be aware of other communities in the space and are able to find the right niche; they are also probably better at writing the kinds of welcome messages, initial posts, etc. that appeal to people interested in the topic.

Second, our work is yet more evidence that communities require different things at different points in their lifecycle. Founders should probably focus on building awareness at first, and worry less about encouraging contributions or regulating behavior.

Finally, we think there are a lot of opportunities for designers to take diverse motivations and goals seriously. This could include matching people by their motivations for using a community, developing dashboards that capture different aspects of success and community health and quality, etc.

Learn More

If you want to learn more about the paper, you have options!

• If you’re at CHI, come to the talk. It will be at 9:45 in room 319 on Tuesday, May 14
• Read the paper (and cite it)!
• Join the conversation on Reddit ( r/RedditEng, r/CompSocial)
• Talk with Sanjay and me on social media