Why do people participate in small online communities?

The number of unique commenters who commented on subreddits in March 2020, for subreddits that had at least 1 comment in the each of the previous 23 months. The “SR” communities are those we drew our interview sample from.

When it comes to online communities, we often assume that bigger is better. Large communities can create robust interactions, have access to broad and extensive body of experiences, and provide many opportunities for connections. As a result, small communities are often thought as failed attempts to build big ones. In reality, most online communities are very small and most small communities remain small throughout their lives.  If growth and a large number of members are so advantageous, why do small communities not only exist but persist in their smallness?

In a recent research study, we investigated why individuals participate in these persistently small online communities by interviewing twenty participants of small subreddits  on Reddit. We asked people about their motivations and explicitly tried to get them to compare their experiences in small subreddits with their experience in larger subreddits. Below we present three of the main things that we discovered through analyzing our conversations.

Size of consistently active subreddits over time (i.e., those with at least one comment per month from April 2018 to March 2020). Subreddits are grouped by their size in April 2018. Lines represent the median size each month, and ribbons show the first and third quartiles.

Informational niches

First, we found that participants saw their small communities as unique spaces for information and interaction. Frequently, small communities are narrower versions or direct offshoots of larger communities. For example, the r/python community is about the programming language Python while the r/learnpython community is a smaller community explicitly for newcomers to the language. 

By being in a smaller, more specific community, our participants described being able to better anticipate the content, audience, and norms: a specific type of content, people who cared about the narrow topic just like them, and expectations of how to behave online. For example, one participant said:

[…] I can probably make a safe assumption that people there more often than not know what they’re talking about. I’ll definitely be much more specific and not try to water questions down with like, my broader scheme of things—I can get as technical as possible, right? If I were to ask like the same question over at [the larger parent community], I might want to give a little bit background on what I’m trying to do, why I’m trying to do it, you know, other things that I’m using, but [in small community], I can just be like, hey, look, I’m trying to use this algorithm for this one thing. Why should I? Or should I not do it for this?

Curating online experiences

More broadly, participants explained their participation in these small communities as part of an ongoing strategy of curating their online experience. Participants described a complex ecosystem of interrelated communities that the small communities sat within, and how the small communities gave them the ability to select very specific topics, decide who to interact with, and manage content consumption.

In this sense, small communities give individuals a semblance of control on the internet. Given the scale of the internet—and a widespread sense of malaise with online hate, toxicity, and harassment—it is possible that controlling the online experience is more important to users than ever. Because of their small size, these small communities were largely free of the vandals and trolls that plague large online communities, and  several participants described their online communities as special spaces to get away from the negativity on the rest of the internet. 

Relationships

Finally, one surprise from our research was what we didn’t find. Previous research led us to predict that people would participate in small communities because they would make it easier to develop friendships with other people. Our participants described being interested in the personal experiences of other group members, but not in building individual relationships with them.

Conclusions

Our research shows that small online communities play an important and underappreciated role. At the individual level, online communities help people to have control over their experiences, curating a set of content and users that is predictable and navigable. At the platform level, small communities seem to have a symbiotic relationship with large communities. By breaking up broader topical niches, small communities likely help to keep a larger set of users engaged.

We hope that this paper will encourage others to take seriously the role of small online communities. They are qualitatively different from large communities, and more empirical and theoretical research is needed in order to understand how communities of different sizes operate and interact in community ecosystems.


A preprint of the paper is available here. We’re excited that this paper has been accepted to CSCW2021 and will be published in the Proceedings of the ACM on Human-Computer Interaction and presented at the conference in November. If you have any questions about this research, please feel free to reach out to one of the authors: Sohyeon Hwang or Jeremy Foote.

Apply to Join the Community Data Science Collective as a PhD student!

It’s Ph.D. application season and the Community Data Science Collective is recruiting! As always, we are looking for talented people to join our research group. Applying to one of the Ph.D. programs that the CDSC faculty members are affiliated with is a great way to do that.

This post provides a very brief run-down on the CDSC, the different universities and Ph.D. programs we’re affiliated with, and what we’re looking for when we review Ph.D. applications. It’s close to the deadline for some of our programs, but we hope this post will still be useful to prospective applicants now and in the future.

Group photo of the collective at a recent virtual retreat.

What is the Community Data Science Collective?

The Community Data Science Collective (or CDSC) is a joint research group of (mostly) quantitative social scientists and designers pursuing research about the organization of online communities, peer production, online communities, and learning and collaboration in social computing systems. We are based at Northwestern University, the University of Washington, Carleton College, the University of North Carolina, Chapel Hill, Purdue University, and a few other places. You can read more about us and our work on our research group blog and on the collective’s website/wiki.

What are these different Ph.D. programs? Why would I choose one over the other?

This year the group includes four faculty principal investigators (PIs) who are actively recruiting PhD students: Aaron Shaw (Northwestern University), Benjamin Mako Hill and Sayamindu Dasgupta (University of Washington in Seattle), and Jeremy Foote (Purdue University). Each of these PIs advise Ph.D. students in Ph.D. programs at their respective universities. Our programs are each described below.

Although we often work together on research and serve as co-advisors to students in each others’ projects, each faculty person has specific areas of expertise and interests. The reasons you might choose to apply to one Ph.D. program or to work with a specific faculty member could include factors like your previous training, career goals, and the alignment of your specific research interests with our respective skills.

At the same time, a great thing about the CDSC is that we all collaborate and regularly co-advise students across our respective campuses, so the choice to apply to or attend one program does not prevent you from accessing the expertise of our whole group. But please keep in mind that our different Ph.D. programs have different application deadlines, requirements, and procedures!

Who is actively recruiting this year?

Given the disruptions and uncertainties associated with the COVID19 pandemic, the faculty PIs are more constrained in terms of whether and how they can accept new students this year. If you are interested in applying to any of the programs, we strongly encourage you to reach out the specific faculty in that program before submitting an application.

Ph.D. Advisors

Sayamindu Dasgupta head shot
Sayamindu Dasgupta

Although he is currently at the University of North Carolina, Sayamindu Dasgupta will starting this year as an Assistant Professor in the Department of Human-Centered Design and Engineering at the University of Washington. Sayamindu’s research focus includes data science education for children and informal learning online—this work involves both system building and empirical studies.

Benjamin Mako Hill

Benjamin Mako Hill is an Assistant Professor of Communication at the University of Washington. He is also an Adjunct Assistant Professor at UW’s Department of Human-Centered Design and Engineering (HCDE), Computer Science and Engineering (CSE) and Information School. Although many of Mako’s students are in the Department of Communication, he has also advised students in all three other departments—although he typically has more limited ability to admit students into those programs. Mako’s research focuses on population-level studies of peer production projects, computational social science, efforts to democratize data science, and informal learning. Mako has also put together a webpage for prospective graduate students with some useful links and information..

Aaron Shaw. (Photo credit: Nikki Ritcher Photography, cc-by-sa)

Aaron Shaw is an Associate Professor in the Department of Communication Studies at Northwestern. In terms of Ph.D. programs, Aaron’s primary affiliations are with the Media, Technology and Society (MTS) and the Technology and Social Behavior (TSB) Ph.D. programs. Aaron also has a courtesy appointment in the Sociology Department at Northwestern, but he has not directly supervised any Ph.D. advisees in that department (yet). Aaron’s current research projects focus on comparative analysis of the organization of peer production communities and social computing projects, participation inequalities in online communities, and empirical research methods.

Jeremy Foote

Jeremy Foote is an Assistant Professor at the Brian Lamb School of Communication at Purdue University. He is affiliated with the Organizational Communication and Media, Technology, and Society programs. Jeremy’s current research focuses on how individuals decide when and in what ways to contribute to online communities, how communities change the people who participate in them, and how both of those processes can help us to understand which things become popular and influential. Most of his research is done using data science methods and agent-based simulations.

What do you look for in Ph.D. applicants?

There’s no easy or singular answer to this. In general, we look for curious, intelligent people driven to develop original research projects that advance scientific and practical understanding of topics that intersect with any of our collective research interests.

To get an idea of the interests and experiences present in the group, read our respective bios and CVs (follow the links above to our personal websites). Specific skills that we and our students tend to use on a regular basis include experience consuming and producing social science and/or social computing (human-computer interaction) research; applied statistics and statistical computing, various empirical research methods, social theory and cultural studies, and more.

Formal qualifications that speak to similar skills and show up in your resume, transcripts, or work history are great, but we are much more interested in your capacity to learn, think, write, analyze, and/or code effectively than in your credentials, test scores, grades, or previous affiliations. It’s graduate school and we do not expect you to show up knowing how to do all the things already.

Intellectual creativity, persistence, and a willingness to acquire new skills and problem-solve matter a lot. We think doctoral education is less about executing a task that someone else hands you and more about learning how to identify a new, important problem; develop an appropriate approach to solving it; and explain all of the above and why it matters so that other people can learn from you in the future. Evidence that you can or at least want to do these things is critical. Indications that you can also play well with others and would make a generous, friendly colleague are really important too.

All of this is to say, we do not have any one trait or skill set we look for in prospective students. We strive to be inclusive along every possible dimension. Each person who has joined our group has contributed unique skills and experiences as well as their own personal interests. We want our future students and colleagues to do the same.

Now what?

Still not sure whether or how your interests might fit with the group? Still have questions? Still reading and just don’t want to stop? Follow the links above for more information. Feel free to send at least one of us an email. We are happy to try to answer your questions and always eager to chat.

Community Data Science Collective Research at DebConf 2021

Debian is one of the oldest, largest, and most influential peer production communities and has produced an operating system used by millions for over the last three decades. DebConf is that community’s annual meeting. This year, the Community Data Science Collective was out in force at Debian’s virtual conference to present several Debian-focused research projects that we’ve been working on.

First, Wm Salt Hale presented work from his master thesis project on “Resilience in FLOSS: Do founder decisions impact development activity after crisis events?” His work tried to understand the social dynamics behind organizational resilience among free software projects based on what Salt calls “founder decisions.” He did so by estimating the relationship between changes in developer activity after security bugs and testing several theories about how this relationship might vary between permissive and copyleft licensed software packages.

Wm Salt Hale’s presentation plus Q&A. (WebM available)

Next, Kaylea and Salt facilitated a “birds-of-a-feather” get-together session for FLOSS project founders (video is also available).

Finally, Kaylea Champion presented her work with Benjamin Mako Hill on “Detecting At Risk Software in Debian.” Her work described a new technique that involves identifying software packages that are less (or more) high quality than you we might expect given their popularity. You can read more about that work in our blog post from earlier this year.

Kaylea Champion’s presentation plus Q&A. (WebM available)

If you saw either presentation and are interested in continuing the conversation, you are welcome to reach out to us individually ({kaylea OR halew}@uw.edu). You can also follow us on this blog, or follow or engage with us in the Fediverse (@communitydata@social.coop), or on Twitter (@comdatasci).

Future Tools for Youth Data Literacies

Workshop Report From Connected Learning Summit 2021

What are data literacies? What should they be? How can we best support youth in developing them via future tools? On July 13th and July 15th 2021, we held a two-day workshop at the Connected Learning Summit to explore these questions. Over the course of two very-full one-hour sessions, 40 participants from a range of backgrounds got to know each other, shared their knowledge and expertise, and engaged in brainstorming to identify important pressing questions around youth data literacies as well as promising ways to design future tools to support youth in developing them. In this blog post, we provide a full report from our workshop, links to the notes and boards we created during the workshop, and a description of  how anyone can get involved in the community around youth data literacies that we have begun to build.

Caption: We opened our sessions by encouraging participants to share and synthesize what youth data literacies meant to them. This affinity diagram is the result. 

How this workshop came to be

As part of the research team interested in research about learning at the Community Data Science Collective, we have long been fascinated with how youth and adults learn how to ask and answer questions with data  While we have engaged with these questions ourselves by looking to Scratch and Cognimates, we are always curious about how we might design tools to promote youth data literacies in the future in other contexts. 

The Connected Learning Summit is a unique gathering of practitioners, researchers, teachers, educators, industry professionals, and others, all interested in formal and informal learning and the impact of new media on current and future communities of learners. When the Connected Learning Summit put up a call for workshops, we thought this was a great opportunity to engage the broader community on the topic of youth data literacies. 

Several months ago, the four of us (Stefania, Regina, Emilia and Mako) started to brainstorm ideas for potential proposals. We started by listing potential aspects and elements of data literacies such as: finding & curating data, visualizing & analyzing it, programming with data, and engaging in critical reflection. We then started to identify tools that can be used to accomplish each goal and tied to identify opportunities and gaps. See some examples of these tools on our workshop website.

Caption: Workshop core team and co-organizers community. Find out more here http://www.dataliteracies.com/

As part of this process, we  identified a number of leaders in the space. This included people who have built tools like Rahul Bhargava and Catherine D’Ignazio who designed Databasic.io,Andee Rubinwho contributed to CODAP, and Victor Lee who focused on tools that link personal informatics and data. Other leaders included scholars who researched how existing tools are being used to support data literacies, including Tammy Clegg who has researched how college athletes develop data literacy skills, Yasmin Kafai who has looked at e-textile projects, and Camillia Matuk who has done research on data literacy curricula. Happily, all of these leaders agreed to join us as co-organizers for the workshop. 

The workshop and what we learned from it

Our workshop took place on July 13th and July 15th as part of the 2021 Connected Learning Summit. Participants came from diverse backgrounds and the group included academic researchers, industry practitioners, K-12 teachers, and librarians. On the first day we focused on exploring existing learning scenarios designed to promote youth data literacies. On the second day we built on big questions raised in the initial session and brainstormed features for future systems. Both workshop sessions were composed of several breakout sessions. We took notes in a shared editor and encouraged participants to add their ideas and comments on sticky notes on collaborative digital white boards and share their definitions and questions around data literacies. 

Caption: organizers and participants sharing past projects and ideas in a breakout session. 

Day 1 Highlights

On Day 1, we explored a variety of existing tools designed to promote youth data literacies. We had a total of 28 participants who attended the session. We began with a group exercise where we shared their own definitions of youth data literacies before dividing into 3 groups: a group focusing on tools for data visualization and storytelling, a group focusing on block-based tools, and a group focusing on data literacy curricula. In each breakout session, our co-organizers first demonstrated one or two existing tools. Each group then discussed how the demo tool might support a single learning scenario based on the following prompt: “Imagine a six-grader who just learned basic concepts about central tendency, how might she use these tools to apply this concept on real world data?” Each group generated many reflective questions and ideas that would prompt and help inform the design of future data literacies tools. Results of our process are captured in the boards linked below. 

Caption: Activities on Miro boards during the workshop.

Data visualization and storytelling

Click here to see the activities on Miro board for this breakout session. 

 

In the sub-section focusing on data visualization and storytelling, Victor Lee first demonstrated Tinkerplots, a desktop-based software that allows students to explore a variety of visualizations with simple click-button interaction using data in .csv format. Andee Rubin then demonstrated CODAP, a web-based tool similar to Tinkerplots that supports drag-and-drop with data, additional visual representation options including maps, and connection between representations. 

Caption: CODAP and Tinkerplots—two tools demonstrated during the workshop.

We discussed how various features of these tools could support youth data literacies in specific learning scenarios. We saw flexibility as one of the most important factors in tool use, both for learners and teachers. Both tools are topic-agnostic and compatible with any data in .csv format. This allows students to explore data of any topics that interest them. Simplicity in interaction is another important advantage. Students can easily see the links between tabular data and visualizations and try out different representations using simple interactions like drag-and-drop, check boxes, and button clicks. Features of these tools can also support students in performing aggregation on data and telling stories about trends and outliers. 

We further discussed potential learning needs beyond what the current features could support. Before creating visualizations, students may need scaffolds during the process of data collection, as well as in the stage of programming with and preprocessing data. Story telling about the process of working with data was another theme that came up a lot from our discussion. Open questions include how features can be designed to support reproducibility, how we can design scaffolds for students to explain what they are doing with data in diary style stories, and how we can help students narrate what they think about a dataset and why they generate particular visualizations.

Block-based tools

Click here to see the activities on Miro board for this breakout session. 

The breakout section about block-based tools started with PhD candidate Stefania Druga demonstrating a program in Scratch and how users could interact with data using the Scratch Cloud Data. We brainstormed about the kind of data students could collect and explore and the kind of visualization, game-based, or other creative interactions youth could create with the help of block-based tools. As a group, we came up with many creative ideas. For example, students can collect and visualize “the newest COVID tweet at the time you touched” a sensor and make “sound effect every time you count a face-touch.” 

Caption: A Scratch project demonstrated during the workshop made with Cloud Data.

We discussed how interaction with data was part of an enterprise that is larger than any particular digital scaffold. After all, data exploration is embedded in social context and might reflect hot topics and recent trends. For instance, many of our ideas about data explorations were around COVID-19 related data and topics. 

Our group also felt that interaction with data should not be limited to a single digital software. Many scenarios we came up with were centered on personal data collection in physical spaces (e.g., counting the number of times a student touches their own face). This points to a future design direction of how we can connect multiple tools that support interaction in both digital and physical spaces and encourage students to explore questions using different tools. 

A final theme from our discussion was around how we can use block-based tools to allow engagement with data among a wider audience. For example, accessible and interesting activities and experience with block-based tools could be designed so that librarians can get involved in meaningful ways to introduce people to data. 

Data literacy curriculum

Click here to see the activities on Miro board for this breakout session. 

In the breakout section emphasizing on curriculum design, we started with an introduction by Catherine D’Ignazio and Rahul Bhargava on DataBasic.io’s Word Counter: a tool that allows users to paste in text to see word counts in various ways. We also walked through some curricula that the team created to guide students through the process of telling stories with data. 

We talked about how this design was powerful in that it allows students to bring their own data and context, and to share knowledge about what they expect to find. Some of the scenarios we imagined included students analyzing their own writings, favorite songs, and favorite texts, and how they might use data to tell personalized stories from there. The specificity of the task supported by the tool enables students to deepen concepts about data by asking specific questions and looking at different datasets to explore the same question. 

Caption: dataBASIC.io helps users explore data.

We also reflected on the fact that tools provided in Databasic.io are easy to use precisely because they are quite narrowly focused on a specific analytic task. This is a major strength of the tools, as they are intended as transitional bridges to help users develop foundational skills for data analysis. Using these tools should help answer questions, but should also encourage users to ask even more.

This led to a new set of issues discussed during the breakout session: How do we chain collections of small tools that might serve as one part of a data literacies pipeline together? This is where we felt curricular design could really come into play. Rather than having tools that try to “be everything,” using well-designed tools that address one aspect of an analysis can provide more flexibility and freedom to explore. Our group felt that curriculum can help learners reach the most important step in their learning, going from data to story to the bigger world—and to understanding why the data might matter. 

Day 2 Highlights

The goal for the Day 2 of our workshop was to speculate and brainstorm future designs of tools that support youth data literacies. After our tool exploration and discussions on Day 1, three interesting brainstorming questions emerged across the breakout sections described above:

  • How can we close the gap between general purpose tools and specific learning goals?
  • How can we support storytelling using data?
  • How can we support insights into the messiness of data and hidden decisions

We focused on discussing these questions on Day 2. A total of 29 participants attended and we once again divided into breakout groups based on the three questions above. For each brainstorming question, we considered the key questions in terms of the following three sub-questions: What are some helpful tools or features that can help answer the question? What are some pitfalls? And what new ideas can we come up with?

Caption: Workshop activities generated an abundance of ideas.

How can we close the gap between general purpose tools and specific learning goals?

Click here to see the activities on Miro board for this breakout session. 

Often tools designed to solve a range of potential problems. That said, learners attempting to engage in data analysis are frequently faced with extremely specific questions about their analysis and datasets. Where does their data come from? How is it structured? How can it be collected? How do we balance the desire to serve many specific learners’ goals with general tools against the desire to handle specific challenges well?

As one approach, we drew lines between different parts of doing data analysis and frequently required features in different tools. Of course, data analysis is rarely a simple linear process. We also concluded that perhaps not everything needs to happen in one place or with one tool, and that this should be acknowledged and considered during the design process.  We also discussed the importance of providing context within more general data analytic tools. We also talked about how learners need to think about the purpose of their analysis before they consider what tool to use and how, ideally, youth would learn to see patterns in data and to understand the significance of the patterns they find. Finally, we agreed that tools that help students understand the limitations of data and the uncertainty inherent in the data are also important.

Challenges and opportunities for telling stories with data

Click here to see the activities on Miro board for this breakout session. 

In this section, we discussed challenges and opportunities around supporting students to tell stories with data. We talked about enabling students to recognize and represent the backstory of data. Open questions included: How do we make sure learners are aware of bias? And how can we help people recognize and document the decision of what to include and exclude?

As for telling stories about students’ own experience of working with data, collaboration was also a topic that came up frequently. We agreed that narrative with data is never an individual process. We discussed that future tools should be designed to support critique, iteration, and collaboration among storytellers, audiences, and maybe also between tellers and audiences.

Finally, we talked about future directions. This included taking a crowdsourced, community-driven approach to tell stories with data. We also noted that we had seen a lot of research effort to support storytelling about data in visualization systems or computational notebooks. We agreed that storytelling should not be limited to digital format and speculated that future designs could extend the storytelling process to unplugged, physical activities. For example, we can design to encourage students to create artefacts and monuments as part of the data storytelling process. We also talked about designing  to engage people from diverse backgrounds and communities to contribute to and explore data together. 

Challenges and opportunities for helping students to understand the messiness of data

Click here to see the activities on Miro board for this breakout session. 

In this section, we talked about the tension between the need to make data clean and easy to use for students and the need to let youth understand the messiness of real world data. We shared our own experiences helping students engage with real or realistic data. A common way is to engage students in collaborative data production and have them compare the outcomes of a similar analysis between each other. For instance, students can document their weekly groceries and find that different people record the same items under different names. They can then come up with a plan to name things consistently and clean their data.

One very interesting point that came up from our discussion was what we really mean by “messy data.” “Messy,” incomplete, or inconsistent data may be unusable for computers while still comprehensible by humans. Therefore to be able to work with messy data does not only mean to have the skills to preprocess, but also involve the recognition of hidden human decisions and assumptions. 

We came up with many ideas regarding future system design. We suggested designing to support crowdsourced data storytelling. For example, students can each contribute a small piece of documentation about the background of a dataset. Features might also be designed to support students to collect and represent the backstory of data in innovative ways. For example, functions that support the generation of rich media, such as videos, drawings, journal entries, can be embedded into data representation systems. We might also innovate on the way we design the interface of data storage so that students can interact with rich background information and metadata while still keeping the data “clean” for computation.

Next steps & community

We intend for this workshop to be only the beginning of our learning and exploration in the space of youth data literacies. We also hope to continue building the community we built. In particular, we have started a mailing list where we can continue our ongoing discussion. Please feel free to add yourself to the mailing list if you would like to be kept informed about our ongoing activities.

Although the workshop has ended, we have included links to many resources on the workshop website, and we invite you to explore the site. We also encourage you to contribute to a crowdsourced list of papers on data literacies by filling out this form.  


This blog was collaboratively written by Regina Cheng, Stefania Druga, Emilia Gan, and Benjamin Mako Hill.

Stefania Druga is a PhD candidate in the Information School at University of Washington. Her research centers on AI literacy for families and designing tools for interest-based creative coding. In her most recent project, she focuses on building a platform that leverages youth creative confidence via coding with AI agents. 

Regina Cheng is a PhD candidate in the Human Centered Design and Engineering department at University of Washington. Her research centers on broadening and facilitating participation in online informal learning communities. In her most recent work, she focuses on designing for novices’ engagement with data in online communities.

Emilia Gan is a graduate student in the Paul G. Allen School of Computer Science and Engineering (UW-Seattle). Her research explores factors that lead to continued participation of novices in computing.

Benjamin Mako Hill is an Assistant Professor at UW. His research involves democratizing data science—and doing it from time to time as well.

CDSC is hiring a staff person!

Group photo of many of the collective members at a virtual retreat in Spring 2021.

Do you (or someone you know) care about online communities and organizing, scientific research, education, and sharing ideas? We are looking for a person to join us and help grow our research and public impact. The (paid, part-time with benefits) position will focus on responsibilities such as research assistance, research administration, communications and outreach. 

This is a new position and will be the first dedicated staff member with the group. The person who takes the job will shape the role together with us based on their interests and skills.  While we have some ideas about the qualifications that might make somebody a compelling candidate (see below), we are eager to hear from anyone who is willing to get involved, learn on the job, and collaborate with us. You do not need to be an expert or have decades of experience to apply for this job. We aim to value and build on applicants’ experiences.

The position is about half time (25 hours per week) through Northwestern University and could be performed almost entirely remotely (the collective hosts in-person meetings and workshops when public health/safety allows). The salary will start at around $30,000 per year and includes excellent benefits through Northwestern. We’re looking for a minimum 1 year commitment.

Expected responsibilities will likely fall into three areas:

  • Support research execution (example: develop materials to recruit study participants)
  • Research administration (example: manage project tracking, documentation)
  • Community management (example: plan meetings with partner organizations)

Candidates must hold at least a bachelor’s degree. Familiarity with scientific research, project management, higher education, and/or event planning is a plus, as is prior experience in the social or computer sciences, research organizations, online communities, and/or public interest technology and advocacy projects of any kind.

To learn more about the Community Data Science Collective, you should check out our wiki, read previous posts on this blog, and look at some of our recent publications. Please feel free to contact anyone in the group with questions. We are committed to creating a diverse, inclusive, equitable, and accessible work environment within our collective and we look forward to working with someone who shares these values.

Ready to apply? Please do so via this Northwestern University job posting.  We are reviewing applications on a rolling basis and hope to hire someone to begin later this summer.

Community Data Science Collective at ICA 2021

As we do every year, members of the Community Data Science Collective will be presenting work at the International Communication Association (ICA)’s 71st Annual Conference which will take place virtually next week. Due to the asynchronous format of ICA this year, none of the talks will happen at specific times. Although the downside of the virtual conference is that we won’t be able to meet up with you all in person, the good news is that you’ll be able to watch our talks and engage with us on whatever timeline suits you best between May 27 and and 31st.

This year’s offerings from the collective include:

Nathan TeBlunthuis will be presenting work with Benjamin Mako Hill as part of the ICA Computational Methods section on “Time Series and Trends in Communication Research.” The name of their talk is “A Community Ecology Approach for Identifying Competitive and Mutualistic Relationships Between Online Communities.”

Aaron Shaw is presenting a paper on “Participation Inequality in the Gig Economy” on behalf of himself, Floor Fiers and Eszter Hargittai . The talk will be as part of a session organized by the ICA Communication and Technology section on “From Autism to Uber: The Digital Divide and Vulnerable Populations.”

Floor Fiers collaborated with Nathan Walter on a poster titled “Sharing Unfairly: Racial Bias on Airbnb and the Effect of Review Valence.” The poster is part of the interactive poster session of the ICA Ethnicity and Race section.

Nick Hager will be talking about his paper with Aaron Shaw titled “Randomly-Generated Inequality in Online News Communities,” which is part of a high density session on “Social Networks and Influence.”

Finally, Jeremy Foote will be chairing a session on “Cyber Communities: Conflicts and Collaborations” as part of the ICA Communication and Technology division.

We look forward to sharing our research and connecting with you at ICA!

UPDATE: The paper led by Nathan TeBlunthuis won the best paper award from the ICA Computational Methods section! Congratulations, Nate!

A round-up of our recent research

Data (Alice Design, cc-by, via the noun project)

We try to keep this blog updated with new research and presentations from members of the group, but we often fall behind. With that in mind, this post is more of a listicle: 22 things you might not have seen from the CDSC in the past year! We’ve included links to (hopefully un-paywalled copies) of just about everything.

Papers and book chapters

Presentations and panels

  • Champion, Kaylea. (2020) How to build a zombie detector: Identifying software quality problems. Seattle Gnu/Linux Users Conference, November, 2020.
  • Hwang, Sohyeon and Aaron Shaw. (2020) Heterogeneous practices in collective governance. Presented at Collective Intelligence 2020 (CI 2020). Boston & Copenhagen (Virtually held).
  • Shaw, Aaron. The importance of thinking big: Convergence, divergence, and independence among wikis and peer production communities. WIkiResearch Showcase. January 20, 2021.
  • TeBlunthuis Nathan E., Benjamin Mako Hill. Aaron Halfaker. “Algorithmic flags and Identity-Based Signals in Online Community Moderation” Session on Social media 2, International Conference on Computational Social Science (IC2S2 2020), Cambridge, MA, July 19, 2020.
  • TeBlunthuis Nathan E.., Aaron Shaw, *Benjamin Mako Hill. “The Population Ecology of Online Collective Action.” Session on Culture and fairness, International Conference on Computational Social Science (IC2S2 2020), Cambridge, MA, July 19, 2020.
  • TeBlunthuis Nathan E., Aaron Shaw, Benjamin Mako Hill. “The Population Ecology of Online Collective Action.” Session on Collective Action, ACM Conference on Collective Intelligence (CI 2020), Boston, MA, June 18, 2020.

Apply to Join the Community Data Science Collective!

It’s Ph.D. application season and the Community Data Science Collective is recruiting! As always, we are looking for talented people to join our research group. Applying to one of the Ph.D. programs that the CDSC faculty members are affiliated with is a great way to do that.

This post provides a very brief run-down on the CDSC, the different universities and Ph.D. programs we’re affiliated with, and what we’re looking for when we review Ph.D. applications. It’s close to the deadline for some of our programs, but we hope this post will still be useful to prospective applicants now and in the future.

CDSC members at the CDSC group retreat in August 2020 (pandemic virtual edition). Left to right by row, starting at top: Charlie, Mako, Aaron, Carl, Floor, Gabrielle, Stef, Kaylea, Tiwalade, Nate, Sayamindu, Regina, Jeremy, Salt, and Sejal.

What is the Community Data Science Collective?

The Community Data Science Collective (or CDSC) is a joint research group of (mostly) quantitative social scientists and designers pursuing research about the organization of online communities, peer production, online communities, and learning and collaboration in social computing systems. We are based at Northwestern University, the University of Washington, Carleton College, the University of North Carolina, Chapel Hill, Purdue University, and a few other places. You can read more about us and our work on our research group blog and on the collective’s website/wiki.

What are these different Ph.D. programs? Why would I choose one over the other?

Although we have people at other places, this year the group includes four faculty principal investigators (PIs) who are actively recruiting PhD students: Aaron Shaw (Northwestern University), Benjamin Mako Hill (University of Washington in Seattle), Sayamindu Dasgupta (University of North Carolina at Chapel Hill), and Jeremy Foote (Purdue University). Each of these PIs advise Ph.D. students in Ph.D. programs at their respective universities. Our programs are each described below.

Although we often work together on research and serve as co-advisors to students in each others’ projects, each faculty person has specific areas of expertise and interests. The reasons you might choose to apply to one Ph.D. program or to work with a specific faculty member could include factors like your previous training, career goals, and the alignment of your specific research interests with our respective skills.

At the same time, a great thing about the CDSC is that we all collaborate and regularly co-advise students across our respective campuses, so the choice to apply to or attend one program does not prevent you from accessing the expertise of our whole group. But please keep in mind that our different Ph.D. programs have different application deadlines, requirements, and procedures!

Who is actively recruiting this year?

Given the disruptions and uncertainties associated with the COVID19 pandemic, the faculty PIs are more constrained in terms of whether and how they can accept new students this year. If you are interested in applying to any of the programs, we strongly encourage you to reach out the specific faculty in that program before submitting an application.

Ph.D. Advisors

Sayamindu Dasgupta head shot
Sayamindu Dasgupta

Sayamindu Dasgupta is is an Assistant Professor in the School of Information and Library Science at UNC Chapel Hill. Sayamindu’s research focus includes data science education for children and informal learning online—this work involves both system building and empirical studies.

Benjamin Mako Hill

Benjamin Mako Hill is an Assistant Professor of Communication at the University of Washington. He is also an Adjunct Assistant Professor at UW’s Department of Human-Centered Design and Engineering (HCDE) and Computer Science and Engineering (CSE). Although many of Mako’s students are in the Department of Communication, he also advises students in the Department of Computer Science and Engineering, HCDE, and the Information School—although he typically has limited ability to admit students into those programs. Mako’s research focuses on population-level studies of peer production projects, computational social science, efforts to democratize data science, and informal learning.

Aaron Shaw. (Photo credit: Nikki Ritcher Photography, cc-by-sa)

Aaron Shaw is an Associate Professor in the Department of Communication Studies at Northwestern. In terms of Ph.D. programs, Aaron’s primary affiliations are with the Media, Technology and Society (MTS) and the Technology and Social Behavior (TSB) Ph.D. programs. Aaron also has a courtesy appointment in the Sociology Department at Northwestern, but he has not directly supervised any Ph.D. advisees in that department (yet). Aaron’s current research projects focus on comparative analysis of the organization of peer production communities and social computing projects, participation inequalities in online communities, and empirical research methods.

Jeremy Foote

Jeremy Foote is an Assistant Professor at the Brian Lamb School of Communication at Purdue University. He is affiliated with the Organizational Communication and Media, Technology, and Society programs. Jeremy’s current research focuses on how individuals decide when and in what ways to contribute to online communities, and how understanding those decision-making processes can help us to understand which things become popular and influential. Most of his research is done using data science methods and agent-based simulations.

What do you look for in Ph.D. applicants?

There’s no easy or singular answer to this. In general, we look for curious, intelligent people driven to develop original research projects that advance scientific and practical understanding of topics that intersect with any of our collective research interests.

To get an idea of the interests and experiences present in the group, read our respective bios and CVs (follow the links above to our personal websites). Specific skills that we and our students tend to use on a regular basis include experience consuming and producing social science and/or social computing (human-computer interaction) research; applied statistics and statistical computing, various empirical research methods, social theory and cultural studies, and more.

Formal qualifications that speak to similar skills and show up in your resume, transcripts, or work history are great, but we are much more interested in your capacity to learn, think, write, analyze, and/or code effectively than in your credentials, test scores, grades, or previous affiliations. It’s graduate school and we do not expect you to show up knowing how to do all the things already.

Intellectual creativity, persistence, and a willingness to acquire new skills and problem-solve matter a lot. We think doctoral education is less about executing a task that someone else hands you and more about learning how to identify a new, important problem; develop an appropriate approach to solving it; and explain all of the above and why it matters so that other people can learn from you in the future. Evidence that you can or at least want to do these things is critical. Indications that you can also play well with others and would make a generous, friendly colleague are really important too.

All of this is to say, we do not have any one trait or skill set we look for in prospective students. We strive to be inclusive along every possible dimension. Each person who has joined our group has contributed unique skills and experiences as well as their own personal interests. We want our future students and colleagues to do the same.

Now what?

Still not sure whether or how your interests might fit with the group? Still have questions? Still reading and just don’t want to stop? Follow the links above for more information. Feel free to send at least one of us an email. We are happy to try to answer your questions and always eager to chat.

Update on the COVID-19 Digital Observatory

A few months ago we announced the launch of a COVID-19 Digital Observatory in collaboration with Pushshift and with funding from Protocol Labs. As part of this effort over the last several months, we have aggregated and published public data from multiple online communities and platforms. We’ve also been hard at work adding a series of new data sources that we plan to release in the near future.

Transmission electron microscope image of SARS-CoV-2—also known as 2019-nCoV, the not-so-novel-anymore virus that causes COVID-19 (Source: NIH NIAID via Wikimedia Commons, cc-sa 2.0)

More specifically, we have been gathering Search Engine Response Page (SERP) data on a range of COVID-19 related terms on a daily basis. This SERP data is drawn from both Bing and Google and has grown to encompass nearly 300GB of compressed data from four months of daily search engine results, with both PC and mobile results from nearly 500 different queries each day.

We have also continued to gather and publish revision and pageview data for COVID-related pages on English Wikipedia which now includes approximately 22GB of highly compressed data (several dozen gigabytes of compressed revision data each day) from nearly 1,800 different articles—a list that has been growing over time.

In addition, we are preparing releases of COVID-related data from Reddit and Twitter. We are almost done with two datasets from Reddit: a first one that includes all posts and comments from COVID-related subreddits, and a second that includes all posts or comments which include any of a set of COVID-related terms.

For the Twitter data, we are working out details of what exactly we will be able to release, but we anticipate including Tweet IDs and metadata for tweets that include COVID-related terms as well as those associated with hashtags and terms we’ve identified in some of the other data collection. We’re also designing a set of random samples of COVID-related Twitter content that will be useful for a range of projects.

In conjunction with these dataset releases, we have published all of the code to create the datasets as well as a few example scripts to help people learn how to load and access the data we’ve collected. We aim to extend these example analysis scripts in the future as more of the data comes online.

We hope you will take a look at the material we have been releasing and find ways to use it, extend it, or suggest improvements! We are always looking for feedback, input, and help. If you have a COVID-related dataset that you’d like us to publish, or if you would like to write code or documentation, please get in touch!

All of the data, code, and other resources are linked from the project homepage. To receive further updates on the digital observatory, you can also subscribe to our low traffic announcement mailing list.

Sohyeon Hwang awarded NSF Graduate Research Fellowship

Congratulations to Sohyeon Hwang, who will be awarded a prestigious Graduate Research Fellowship (a.k.a., GRFP) from the U.S. National Science Foundation!

photo of Sohyeon Hwang standing somewhere
Sohyeon Hwang standing somewhere.

The award will support Sohyeon’s proposed doctoral research on the complexity of governance practices in online communities. This work will focus on the ways communities heterogeneously fill the gap between rules-as-written (de jure) and rules-as-practiced (de facto) to impact the credibility and effectiveness of online governance work. The main components of this project will center around understanding the significance and role of shared (or conversely, localized) rules across communities; the automated tools utilized by these communities; and how users perceive, experience, and practice heterogeneity in online governance practices.

Sohyeon is a first year Ph.D. student in the Media, Technology & Society Program at Northwestern, advised by Aaron Shaw, and began working with the Community Data Science Collective last summer. She completed her undergraduate degree at Cornell University, where she double-majored in government and information science, focusing on Cold War era politics in the former and data science in the latter.

Sohyeon is currently pursuing graduate coursework, and her ongoing research includes a project comparing governance across several of the largest language editions of Wikipedia as well as work with Dr. Ágnes Horvát developing a project on multi-platform information spread. Recently, she has also taken a lead role in the efforts by CDSC and Pushshift to create a Digital Observatory for COVID-19 information resources.