Community Data Science Collective

Leaders and scholars of online communities tend of think of community growth as the aggregate effect of inexperienced individuals arriving one-by-one. However, there is increasing evidence that growth in many online communities today involves newcomers arriving in groups with previous experience together in other communities. This difference has deep implications for how we think about the process of integrating newcomers. Instead of focusing only on individual socialization into the group culture, we must also understand how to manage mergers of existing groups with distinct cultures. Unfortunately, online community mergers have, to our knowledge, never been studied systematically.

To better understand mergers, I spent six months in 2017 conducting ethnographic participant observation in two World of Warcraft raid guilds planning and undergoing mergers. The results—visible in the attendance plot below—shows that the top merger led to a thriving and sustainable community while the bottom merger led to failure and the eventual dissolution of the group. Why did one merger succeed while the other failed? What can managers of other communities learn from these examples?

In my new paper that will be published in the Proceedings of of the ACM Conference on Computer-supported Cooperative Work and Social Computing (CSCW) and that I will present in New Jersey next month, my coauthors and I try to answer these questions.

In my research setting, World of Warcraft (WoW), players form organized groups called “guilds” to take on the game’s toughest bosses in virtual dungeons that are called “raids.” Raids can be extremely challenging, and they require a large number of players to be successful. Below is a video demonstrating the kind of communication and coordination needed to be successful as a raid team in WoW.

Because participation in a raid guild requires time, discipline, and emotional investment, raid guilds are constantly losing members and recruiting new ones to resupply their ranks. One common strategy for doing so is arranging formal mergers. My study involved following two such groups as they completed mergers. To collect data for my study, I joined both groups, attended and recorded all activities, took copious field notes, and spent hours interviewing leaders.

Although I did not anticipate the divergent outcomes shown in the figure above when I began, I analyzed my data with an eye toward identifying themes that might point to reasons for the success of one merger and the failure of the other. The answers that emerged from my analysis suggest that the key differences that led one merger to be successful and the other to fail revolved around differences in the ways that the two mergers managed organizational culture. This basic insight is supported by a body of research about organizational culture in firms but seem to have not made it onto the radar of most members or scholars of online communities. My coauthors and I think more attention to the role that organizational culture plays in online communities is essential.

We found evidence of cultural incompatibility in both mergers and it seems likely that some degree of cultural clashes is inevitable in any merger. The most important result of our analysis are three observations we drew about specific things that the successful merger did to effectively manage organizational culture. Drawn from our analysis, these themes point to concrete things that other communities facing mergers—either formal or informal—can do.

First, when planning mergers, groups can strategically select other groups with similar organizational culture. The successful merger in our study involved a carefully planned process of advertising for a potential merger on forums, testing out group compatibility by participating in “trial” raid activities with potential guilds, and selecting the guild that most closely matched their own group’s culture. In our settings, this process helped prevent conflict from emerging and ensured that there was enough common ground to resolve it when it did.

Second, leaders can plan intentional opportunities to socialize members of the merged or acquired group. The leaders of the successful merger held community-wide social events in the game to help new members learn their community’s norms. They spelled out these norms in a visible list of rules. They even included the new members in both the brainstorming and voting process of changing the guild’s name to reflect that they were a single, new, cohesive unit. The leaders of the failed merger lacked any explicitly stated community rules, and opportunities for socializing the members of the new group were virtually absent. Newcomers from the merged group would only learn community norms when they broke one of the unstated social codes.

Third and finally, our study suggested that social activities can be used to cultivate solidarity between the two merged groups, leading to increased retention of new members. We found that the successful guild merger organized an additional night of activity that was socially-oriented. In doing so, they provided a setting where solidarity between new and existing members can cultivate and motivate their members to stick around and keep playing with each other — even when it gets frustrating.

Our results suggest that by preparing in advance, ensuring some degree of cultural compatibility, and providing opportunities to socialize newcomers and cultivate solidarity, the potential for conflict resulting from mergers can be mitigated. While mergers between firms often occur to make more money or consolidate resources, the experience of the failed merger in our study shows that mergers between online communities put their entire communities at stake. We hope our work can be used by leaders in online communities to successfully manage potential conflict resulting from merging or acquiring members of other groups in a wide range of settings.

Much more detail is available our paper which will be published open access and which is currently available as a preprint.

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Both this blog post and  the paper it is based on are collaborative work by Charles Kiene from the University of Washington, Aaron Shaw from Northwestern University, and Benjamin Mako Hill from the University of Washington. We are also thrilled to mention that the paper received a Best Paper Honorable Mention award at CSCW 2018!

Couchsurfing and Airbnb are websites that connect people with an extra guest room or couch with random strangers on the Internet who are looking for a place to stay. Although Couchsurfing predates Airbnb by about five years, the two sites are designed to help people do the same basic thing and they work in extremely similar ways. They differ, however, in one crucial respect. On Couchsurfing, the exchange of money in return for hosting is explicitly banned. In other words, couchsurfing only supports the social exchange of hospitality. On Airbnb, users must use money: the website is a market on which people can buy and sell hospitality.

The figure above compares the number of people with at least some trust or verification on both Couchsurfing and Airbnb based on when each user signed up. The picture, as I have argued elsewhere, reflects a broader pattern that has occurred on the web over the last 15 years. Increasingly, social-based systems of production and exchange, many like Couchsurfing created during the first decade of the Internet boom, are being supplanted and eclipsed by similar market-based players like Airbnb.

In a paper led by Max Klein that was recently published and will be presented at the ACM Conference on Computer-supported Cooperative Work and Social Computing (CSCW) which will be held in Jersey City in early November 2018, we sought to provide a window into what this change means and what might be at stake. At the core of our research were a set of interviews we conducted with “dual-users” (i.e. users experienced on both Couchsurfing and Airbnb). Analyses of these interviews pointed to three major differences, which we explored quantitatively from public data on the two sites.

First, we found that users felt that hosting on Airbnb appears to require higher quality services than Couchsurfing. For example, we found that people who at some point only hosted on Couchsurfing often said that they did not host on Airbnb because they felt that their homes weren’t of sufficient quality. One participant explained that:

“I always wanted to host on Airbnb but I didn’t actually have a bedroom that I felt would be sufficient for guests who are paying for it.”

An another interviewee said:

“If I were to be paying for it, I’d expect a nice stay. This is why I never Airbnb-hosted before, because recently I couldn’t enable that [kind of hosting].”

We conducted a quantitative analysis of rates of Airbnb and Couchsurfing in different cities in the United States and found that median home prices are positively related to number of per capita Airbnb hosts and a negatively related to the number of Couchsurfing hosts. Our exploratory models predicted that for each $100,000 increase in median house price in a city, there will be about 43.4 more Airbnb hosts per 100,000 citizens, and 3.8 fewer hosts on Couchsurfing.

A second major theme we identified was that, while Couchsurfing emphasizes people, Airbnb places more emphasis on places. One of our participants explained:

“People who go on Airbnb, they are looking for a specific goal, a specific service, expecting the place is going to be clean […] the water isn’t leaking from the sink. I know people who do Couchsurfing even though they could definitely afford to use Airbnb every time they travel, because they want that human experience.”

In a follow-up quantitative analysis we conducted of the profile text from hosts on the two websites with a commonly-used system for text analysis called LIWC, we found that, compared to Couchsurfing, a lower proportion of words in Airbnb profiles were classified as being about people while a larger proportion of words were classified as being about places.

Finally, our research suggested that although hosts are the powerful parties in exchange on Couchsurfing, social power shifts from hosts to guests on Airbnb. Reflecting a much broader theme in our interviews, one of our participants expressed this concisely, saying:

“On Airbnb the host is trying to attract the guest, whereas on Couchsurfing, it works the other way round. It’s the guest that has to make an effort for the host to accept them.”

Previous research on Airbnb has shown that guests tend to give their hosts lower ratings than vice versa. Sociologists have suggested that this asymmetry in ratings will tend to reflect the direction of underlying social power balances.

We both replicated this finding from previous work and found that, as suggested in our interviews, the relationship is reversed on Couchsurfing. As shown in the figure above, we found Airbnb guests will typically give a less positive review to their host than vice-versa while in Couchsurfing guests will typically a more positive review to the host.

As Internet-based hospitality shifts from social systems to the market, we hope that our paper can point to some of what is changing and some of what is lost. For example, our first result suggests that less wealthy participants may be cut out by market-based platforms. Our second theme suggests a shift toward less human-focused modes of interaction brought on by increased “marketization.” We see the third theme as providing somewhat of a silver-lining in that shifting power toward guests was seen by some of our participants as a positive change in terms of safety and trust in that guests. Travelers in unfamiliar places often are often vulnerable and shifting power toward guests can be helpful.

Although our study is only of Couchsurfing and Airbnb, we believe that the shift away from social exchange and toward markets has broad implications across the sharing economy. We end our paper by speculating a little about the generalizability of our results. I have recently spoken at much more length about the underlying dynamics driving the shift we describe in my recent LibrePlanet keynote address.

More details are available in our paper which we have made available as a preprint on our website. The final version is behind a paywall in the ACM digital library.

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I’m spending the 2018-2019 academic year as a fellow at the Center for Advanced Study in the Behavioral Sciences (CASBS) at Stanford.

Every CASBS study is labeled with a list of  “ghosts” who previously occupied the study. This year, I’m spending the year in Study 50 where I’m haunted by an incredible cast that includes many people whose scholarship has influenced and inspired me.

Foremost among this group is Study 50’s third occupant: Claude Shannon.¹

At 21 years old, Shannon’s masters thesis (sometimes cited as the most important masters thesis in history) proved that electrical circuits could encode any relationship expressible in Boolean logic and opened the door to digital computing. Incredibly, this is almost never cited as Shannon’s most important contribution. That came in 1948 when he published a paper titled A Mathematical Theory of Communication which effectively created the field of information theory. Less than a decade after its publication, Aleksandr Khinchin (the mathematician behind my favorite mathematical constant) described the paper saying:

Rarely does it happen in mathematics that a new discipline achieves the character of a mature and developed scientific theory in the first investigation devoted to it…So it was with information theory after the work of Shannon.

As someone whose own research is seeking to advance computation and mathematical study of communication, I find it incredibly propitious to be sharing a study with Shannon.

Although I teach in a communication department, I know Shannon from my background in computing. I’ve always found it curious that, despite the fact Shannon’s 1948 paper is almost certainly the most important single thing ever published with the word “communication” in its title, Shannon is rarely taught in communication curricula is sometimes completely unknown to communication scholars.

In this regard, I’ve thought a lot about this passage in Robert’s Craig’s  influential article “Communication Theory as a Field” which argued:

In establishing itself under the banner of communication, the discipline staked an academic claim to the entire field of communication theory and research—a very big claim indeed, since communication had already been widely studied and theorized. Peters writes that communication research became “an intellectual Taiwan-claiming to be all of China when, in fact, it was isolated on a small island” (p. 545). Perhaps the most egregious case involved Shannon’s mathematical theory of information (Shannon & Weaver, 1948), which communication scholars touted as evidence of their field’s potential scientific status even though they had nothing whatever to do with creating it, often poorly understood it, and seldom found any real use for it in their research.

In preparation for moving into Study 50, I read a new biography of Shannon by Jimmy Soni and Rob Goodman and was excited to find that Craig—although accurately describing many communication scholars’ lack of familiarity—almost certainly understated the importance of Shannon to communication scholarship.

For example, the book form of Shannon’s 1948 article was published by University Illinois on the urging of and editorial supervision of Wilbur Schramm (one of the founders of modern mass communication scholarship) who was a major proponent of Shannon’s work. Everett Rogers (another giant in communication) devotes a chapter of his “History of Communication Studies”² to Shannon and to tracing his impact in communication. Both Schramm and Rogers built on Shannon in parts of their own work. Shannon has had an enormous impact, it turns out, in several subareas of communication research (e.g., attempts to model communication processes).

Although I find these connections exciting. My own research—like most of the rest of communication—is far from the substance of technical communication processes at the center of Shannon’s own work. In this sense, it can be a challenge to explain to my colleagues in communication—and to my fellow CASBS fellows—why I’m so excited to be sharing a space with Shannon this year.

Upon reflection, I think it boils down to two reasons:

1. Shannon’s work is both mathematically beautiful and incredibly useful. His seminal 1948 article points to concrete ways that his theory can be useful in communication engineering including in compression, error correcting codes, and cryptography. Shannon’s focus on research that pushes forward the most basic type of basic research while remaining dedicated to developing solutions to real problems is a rare trait that I want to feature in my own scholarship.
2. Shannon was incredibly playful. Shannon played games, juggled constantly, and was always seeking to teach others to do so. He tinkered, rode unicycles, built a flame-throwing trumpet, and so on. With Marvin Minsky, he invented the “ultimate machine”—a machine that’s only function is to turn itself off—which he kept on his desk.

   

I have no misapprehension that I will accomplish anything like Shannon’s greatest intellectual achievements during my year at CASBS. I do hope to be inspired by Shannon’s creativity, focus on impact, and playfulness. In my own little ways, I hope to build something at CASBS that will advance mathematical and computational theory in communication in ways that Shannon might have appreciated.

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In 1965, Bruce Tuckman proposed a “developmental sequence in small groups.” According to his influential theory, most successful groups go through four stages with rhyming names:

1. Forming: Group members get to know each other and define their task.
2. Storming: Through argument and disagreement, power dynamics emerge and are negotiated.
3. Norming: After conflict, groups seek to avoid conflict and focus on cooperation and setting norms for acceptable behavior.
4. Performing: There is both cooperation and productive dissent as the team performs the task at a high level.

Fortunately for organizational science, 1965 was hardly the last stage of development for Tuckman’s theory!

Twelve years later, Tuckman suggested that adjourning or mourning reflected potential fifth stages (Tuckman and Jensen 1977). Since then, other organizational researchers have suggested other stages including transforming and reforming (White 2009), re-norming (Biggs), and outperforming (Rickards and Moger 2002).

What does the future hold for this line of research?

To help answer this question, we wrote a regular expression to identify candidate words and placed the full list is at this page in the Community Data Science Collective wiki.

The good news is that despite the active stream of research producing new stages that end or rhyme with -orming, there are tons of great words left!

For example, stages in a group’s development might include:

• Scorning: In this stage, group members begin mocking each other!
• Misinforming: Groups that reach this stage start producing fake news.
• Shoehorning: These groups try to make their products fit into ridiculous constraints.
• Chloroforming: Groups become languid and fatigued?

One benefit of keeping our list in the wiki is that the organizational research community can use it to coordinate! If you are planning to use one of these terms—or if you know of a paper that has—feel free to edit the page in our wiki to “claim” it!

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Although credit for this post goes primarily to Jeremy Foote and Benjamin Mako Hill, the other Community Data Science Collective members can’t really be called blameless in the matter either.

For the second year, Matt Salganik and Chris Bail are running a two-week Summer Institute in Computational Social Science at Duke Univeristy. The goal of the institute is to bring social scientists and data scientists together to learn about computational social science, which can be described as a merger of their two fields.

This year, there are seven partner locations where local students livestream the activities from Duke and learn from local computational social scientists.  Both of our universities are among the partner locations.

At the University of Washington, Kaylea and Charlie have both been accepted as participants in the UW summer institute. At Northwestern University, Jeremy is helping to organize SICSS Chicago.

Much of the work that we do in the Community Data Science Collective could be considered computational social science, and we are excited about the potential for  computational methods in social science. This is a great program for helping to disseminate computational social science approaches and train the next generation of computational social scientists. The Community Data Science Collective is happy to be a sponsor of the Chicago partner location.

The School of Information and Library Sciences (SILS) at the University of North Carolina in Chapel Hill announced this week that the Community Data Science Collective’s very own Sayamindu Dasgupta will be joining their faculty as a tenure-track assistant professor. The announcement from SILS has much more detail and makes very clear that UNC is thrilled to have him join their faculty.

UNC has has every reason to be excited. Sayamindu has been making our research collective look good for several years. Much of this is obvious in the pile of papers and awards he’ s built. In less visible roles, Sayamindu has helped us build infrastructure, mentored graduate and undergraduate students in the group, and has basically just been  joy to have around.

Those of us that work in the Community Data Lab at UW is going to miss having Sayamindu around. Chapel Hill is very, very lucky to have him.

Jeremy Foote, Nate TeBlunthuis, and Mako Hill are in Prague this week for the  International Communication Association’s 2018 annual meeting.

The collective has three things on the conference program this year:

• Fri, May 25, 9:30 to 10:45, Hilton Prague, LL, Vienna: An Agent-Based Model of Online Community Joining as part of the Computational Methods section paper session on “Agent-Based Modeling for Communication Research” — Jeremy Foote (presenting), Benjamin Mako Hill and Nathan TeBlunthuis
• Fri, May 25, 12:30 to 13:45, Hilton Prague, LL, Congress Hall II – Exhibit Hall/Posters: Revisiting ‘The Rise and Decline’ in a Population of Peer Production Projects as part of the Information Systems section’s poster session “ICA Interactive Paper/Poster Session I” —Nathan TeBlunthuis (presenting), Aaron Shaw, and Benjamin Mako Hill
• Mon, May 28, 9:30 to 10:45, Hilton Prague, M, Palmovka: Theory Building Beyond Communities: Population-Level Research in the Computational Methods section’s panel on “Communication in the Networked Age: A Discussion of Theory Building through Data-Driven Research” — Benjamin Mako Hill (presenting) and Aaron Shaw

We look forward to sharing our research and socializing with you at ICA! Please be in touch if you’re around and want to meet up!

Seymour Papert is credited as saying that tools to support learning should have “high ceilings” and “low floors.” The phrase is meant to suggest that tools should allow learners to do complex and intellectually sophisticated things but should also be easy to begin using quickly. Mitchel Resnick extended the metaphor to argue that learning toolkits should also have “wide walls” in that they should appeal to diverse groups of learners and allow for a broad variety of creative outcomes. In a new paper, Benjamin Mako Hill and I attempted to provide the first empirical test of Resnick’s wide walls theory. Using a natural experiment in the Scratch online community, we found causal evidence that “widening walls” can, as Resnick suggested, increase both engagement and learning.

Over the last ten years, the “wide walls” design principle has been widely cited in the design of new systems. For example, Resnick and his collaborators relied heavily on the principle in the design of the Scratch programming language. Scratch allows young learners to produce not only games, but also interactive art, music videos, greetings card, stories, and much more. As part of that team, I was guided by “wide walls” principle when I designed and implemented the Scratch cloud variables system in 2011-2012.

While designing the system, I hoped to “widen walls” by supporting a broader range of ways to use variables and data structures in Scratch. Scratch cloud variables extend the affordances of the normal Scratch variable by adding persistence and shared-ness. A simple example of something possible with cloud variables, but not without them, is a global high-score leaderboard in a game (example code is below). After the system was launched, I saw many young Scratch users using the system to engage with data structures in new and incredibly creative ways.

Although these examples reflected powerful anecdotal evidence, I was also interested in using quantitative data to reflect the causal effect of the system. Understanding the causal effect of a new design in real world settings is a major challenge. To do so, we took advantage of a “natural experiment” and some clever techniques from econometrics to measure how learners’ behavior changed when they were given access to a wider design space.

Understanding the design of our study requires understanding a little bit about how access to the Scratch cloud variable system is granted. Although the system has been accessible to Scratch users since 2013, new Scratch users do not get access immediately. They are granted access only after a certain amount of time and activity on the website (the specific criteria are not public). Our “experiment” involved a sudden change in policy that altered the criteria for who gets access to the cloud variable feature. Through no act of their own, more than 14,000 users were given access to feature, literally overnight. We looked at these Scratch users immediately before and after the policy change to estimate the effect of access to the broader design space that cloud variables afforded.

We found that use of data-related features was, as predicted, increased by both access to and use of cloud variables. We also found that this increase was not only an effect of projects that use cloud variables themselves. In other words, learners with access to cloud variables—and especially those who had used it—were more likely to use “plain-old” data-structures in their projects as well.

The graph below visualizes the results of one of the statistical models in our paper and suggests that we would expect that 33% of projects by a prototypical “average” Scratch user would use data structures if the user in question had never used used cloud variables but that we would expect that 60% of projects by a similar user would if they had used the system.

It is important to note that the estimated effective above is a “local average effect” among people who used the system because they were granted access by the sudden change in policy (this is a subtle but important point that we explain this in some depth in the paper). Although we urge care and skepticism in interpreting our numbers, we believe our results are encouraging evidence in support of the “wide walls” design principle.

Of course, our work is not without important limitations. Critically, we also found that rate of adoption of cloud variables was very low. Although it is hard to pinpoint the exact reason for this from the data we observed, it has been suggested that widening walls may have a potential negative side-effect of making it harder for learners to imagine what the new creative possibilities might be in the absence of targeted support and scaffolding. Also important to remember is that our study measures “wide walls” in a specific way in a specific context and that it is hard to know how well our findings will generalize to other contexts and communities. We discuss these caveats, as well as our methods, models, and theoretical background in detail in our paper which now available for download as an open-access piece from the ACM digital library.

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This blog post, and the open access paper that it describes, is a collaborative project with Benjamin Mako Hill. Financial support came from the eScience Institute and the Department of Communication at the University of Washington. Quantitative analyses for this project were completed using the Hyak high performance computing cluster at the University of Washington.

This graph shows the number of people contributing to Wikipedia over time:

The figure comes from “The Rise and Decline of an Open Collaboration System,” a well-known 2013 paper that argued that Wikipedia’s transition from rapid growth to slow decline in 2007 was driven by an increase in quality control systems. Although many people have treated the paper’s finding as representative of broader patterns in online communities, Wikipedia is a very unusual community in many respects. Do other online communities follow Wikipedia’s pattern of rise and decline? Does increased use of quality control systems coincide with community decline elsewhere?

In a paper I am presenting Thursday morning at  the Association for Computing Machinery (ACM) Conference on Human Factors in Computing Systems (CHI),  a group of us have replicated and extended the 2013 paper’s analysis in 769 other large wikis. We find that the dynamics observed in Wikipedia are a strikingly good description of the average Wikia wiki. They appear to reoccur again and again in many communities.

The original “Rise and Decline” paper (I’ll abbreviate it “RAD”) was written by Aaron Halfaker, R. Stuart Geiger, Jonathan T. Morgan, and John Riedl. They analyzed data from English Wikipedia and found that Wikipedia’s transition from rise to decline was accompanied by increasing rates of newcomer rejection as well as the growth of bots and algorithmic quality control tools. They also showed that newcomers whose contributions were rejected were less likely to continue editing and that community policies and norms became more difficult to change over time, especially for newer editors.

Our paper, just published in the CHI 2018 proceedings, replicates most of RAD’s analysis on a dataset of 769 of the  largest wikis from Wikia that were active between 2002 to 2010.  We find that RAD’s findings generalize to this large and diverse sample of communities.

I can walk you through some of the key findings. First, the growth trajectory of the average wiki in our sample is similar to that of English Wikipedia. As shown in the figure below, an initial period of growth stabilizes and leads to decline several years later.

We also found that newcomers on Wikia wikis were reverted more and continued editing less. As on Wikipedia, the two processes were related. Similar to RAD, we also found that newer editors were more likely to have their contributions to the “project namespace” (where policy pages are located) undone as wikis got older. Indeed, the specific estimates from our statistical models are very similar to RAD’s for most of these findings!

There were some parts of the RAD analysis that we couldn’t reproduce in our context. For example, there are not enough bots or algorithmic editing tools in Wikia to support statistical claims about their effects on newcomers.

At the same time, we were able to do some things that the RAD authors could not.  Most importantly, our findings discount some Wikipedia-specific explanations for a rise and decline. For example, English Wikipedia’s decline coincided with the rise of Facebook, smartphones, and other social media platforms. In theory, any of these factors could have caused the decline. Because the wikis in our sample experienced rises and declines at similar points in their life-cycle but at different points in time, the rise and decline findings we report seem unlikely to be caused by underlying temporal trends.

The big communities we study seem to have consistent “life cycles” where stabilization and/or decay follows an initial period of growth. The fact that the same kinds of patterns happen on English Wikipedia and other online groups implies a more general set of social dynamics at work that we do not think existing research (including ours) explains in a satisfying way. What drives the rise and decline of communities more generally? Our findings make it clear that this is a big, important question that deserves more attention.

We hope you’ll read the paper and get in touch by commenting on this post or emailing me if you’d like to learn or talk more. The paper is available online and has been published under an open access license. If you really want to get into the weeds of the analysis, we will soon publish all the data and code necessary to reproduce our work in a repository on the Harvard Dataverse.

I will be presenting the project this week at CHI in Montréal on Thursday April 26 at 9am in room 517D.  For those of you not familiar with CHI, it is the top venue for Human-Computer Interaction. All CHI submissions go through double-blind peer review and the papers that make it into the proceedings are considered published (same as journal articles in most other scientific fields). Please feel free to cite our paper and send it around to your friends!

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This blog post, and the open access paper that it describes, is a collaborative project with Aaron Shaw, and Benjamin Mako Hill. Financial support came from the US National Science Foundation (grants IIS-1617129,  IIS-1617468, and GRFP-2016220885 ), Northwestern University, the Center for Advanced Study in the Behavioral Sciences at Stanford University, and the University of Washington. This project was completed using the Hyak high performance computing cluster at the University of Washington.

If you are at the University of Washington (or not at UW but in Seattle) and are interested in seeing what we’re up to, you can join us for a Community Data Science Collective “open lab” this Friday (April 6th) 3-5pm in our new lab space (CMU 306). Collective members from Northwestern University will be in town as well, so there’s even more reason to come!

The open lab is an opportunity to learn about our research, catch up over snacks and beverages, and pick up a sticker or two. We will have no presentations but several posters describing projects we are working on.