Protocol Labs works to improve internet technologies through open source protocols, systems, and tools. The organization initially grew out of efforts to apply blockchain tools to support distributed file sharing infrastructure. Their research group, Protocol Labs Research, created the COVID-19 Open Innovation Grants program “to surface and support open-source projects working on tools to help humanity through present and future pandemics.”
In the case of the COVID-19 Digital Observatory, we plan to use the funds provided by the award to build out the resources we have already started to aggregate and release. In particular, we will build additional infrastructure to process and archive data from Reddit and other social media sources as well as search engine results pages (SERPs) for COVID-related queries.
In addition to folks in the collective, the proposal was successful through the efforts of Jason Baumgartner from Pushshift, who is co-leading the observatory work, as well as Marysia Galent, Research Administrator at Northwestern University, whose expert guidance helped make the grant application possible.
A paper recently published at CSCW describes the results of a forensic qualitative analysis of contributions made to Wikipedia through the anonymous browsing system Tor. The project was conducted collaboratively with researchers from Drexel, NYU, and the University of Washington and complements a quantitative analysis of the same data we also published to provide a rich qualitative picture of what anonymity-seekers are trying to do when they contribute to Wikipedia. The work also shows how the ability to stay anonymous can play a important role in facilitating certain types of contributions to online knowledge bases like Wikipedia.
Many individuals use Tor to reduce their visibility to widespread internet surveillance.
Media reports often describe how online platforms are tracking us. That said, trying to live our lives online without leaving a trail of our personal information can be difficult because many services can’t be used without an account and systems that protect privacy are often blocked. One popular approach to protecting our privacy online involves using the Tor network. Tor protects users from being identified by their IP address which can be tied to a physical location. However, if you’d like to contribute to Wikipedia using Tor, you’ll run into a problem. Although most IP addresses can edit without an account, Tor users are blocked from editing.
Tor users attempting to contributing to Wikipedia are shown a screen that informs them that they are not allowed to edit Wikipedia.
Other research by my team has shown that Wikipedia’s attempt to block Tor is imperfect and that some people have been able to edit despite the ban. This work also built a dataset of more than 11,000 contributions made to Wikipedia via Tor and used quantitative analysis to show that the contributions of people using Tor were about the same quality as contributions from other new editors and other contributors without accounts. Of course, given the unusual circumstances Tor-based contributors faced, we wondered if a deeper look into the content of their edits might tell us more about their motives and the kinds of contributions they seek to make. I led a qualitative investigation that sought to explore these questions.
Given the challenges of studying anonymity seekers, we designed a novel “forensic” qualitative approach that was inspired by the techniques common in the practice of computer security as well as criminal investigation. We applied to this new technique to a sample of 500 different editing sessions and sorted each session into a category based on what the editor seemed to be intending to do.
Most of the contributions we found fell into one of the two following categories:
Many contributions were quotidian attempts to add to the encyclopedia. Tor-based editors added facts, they fixed typos, and they updated train schedules. There’s no way to know if these individuals knew that they were just getting lucky in their ability to edit or if they were patiently reloading to evade the ban.
Second, we found harassing comments and vandalism. Unwelcome conduct is common in online environments, and sometimes more common when the likelihood of being identified is decreased. Some of the harassing comments we observed were direct responses to being banned as a Tor user.
Although these were most of what we observed, we also found evidence of several types of contributor intent:
We observed activism, as when a contributor tried to bring attention to journalistic accounts of environmental and human rights abuses being committed by a mining company, only to have editors traceable to the mining company repeatedly remove their edits. Another example included an editor trying to diminish the influence of alternative medicine proponents.
We also observed quality maintenance activities when editors used Wikipedia’s rules about appropriate sourcing to remove personal websites being cited in conspiracy theories.
We saw edit wars with Tor editors participating in a back-and-forth removal and replacement of content as part of a dispute, in some cases countering the work of an experienced Wikipedia editor who even other experienced editors had gauged to be biased.
Finally, we saw Tor-based editors participating in non-article discussions such as investigations of administrator misconduct, and protesting the mistrust of Tor editors by the Wikipedia platform.
An exploratory mapping of our themes in terms of the value a type of contribution represents to the Wikipedia community and the importance of anonymity in facilitating it. Anonymity protecting tools play a critical role in facilitating contributions on the right side of the figure while edits on the left are more likely to occur even when anonymity is impossible. Contributions toward the top reflect valuable forms of participation in Wikipedia while edits on the bottom reflect damage.
In all, these themes led us to reflect on how the risks that individuals face when contributing to online communities are sometimes out of alignment with the risks the communities face by accepting their work. Expressing minoritized perspectives, maintaining community standards even when you may be targeted by the rulebreaker, highlighting injustice or acting as a whistleblower can be very risky for an individual, and may not be possible without privacy protections. Of course, in platforms seeking to support the public good, such knowledge and accountability may be crucial.
This project was conducted by Kaylea Champion, Nora McDonald, Stephanie Bankes, Joseph Zhang, Rachel Greenstadt, Andrea Forte, and Benjamin Mako Hill. This work was supported by the National Science Foundation (awards CNS-1703736 and CNS-1703049) and included the work of two undergraduates supported through an NSF REU supplement.
Paper Citation: Kaylea Champion, Nora McDonald, Stephanie Bankes, Joseph Zhang, Rachel Greenstadt, Andrea Forte, and Benjamin Mako Hill. 2019. A Forensic Qualitative Analysis of Contributions to Wikipedia from Anonymity Seeking Users. Proceedings of the ACM on Human-Computer Interactaction. 3, CSCW, Article 53 (November 2019), 26 pages. https://doi.org/10.1145/3359155
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.
The Community Data Science Collective, in collaboration with Pushshift and others, is launching a new collaborative project to create a digital observatory for socially produced COVID-19 information. The observatory has already begun the process of collecting, and aggregating public data from multiple online communities and platforms. We are publishing reworked versions of these data in forms that are well-documented and more easily analyzable by researchers with a range of skills and computation resources. We hope that these data will facilitate analysis and interventions to improve the quality of socially produced information and public health.
Transmission electron microscope image of SARS-CoV-2—also known as 2019-nCoV, the virus that causes COVID-19 (Source: NIH NIAID via Wikimedia Commons, cc-sa 2.0).
During crises such as the current COVID-19 pandemic, many people turn to the Internet for information, guidance, and help. Much of what they find is socially produced through online forums, social media, and knowledge bases like Wikipedia. The quality of information in these data sources varies enormously and users of these systems may receive information that is incomplete, misleading, or even dangerous. Efforts to improve this are complicated by difficulties in discovering where people are getting information and in coordinating efforts to focus on refining the more important information sources. There are number of researchers with the skills and knowledge to address these issues, but who may struggle to gather or process social data. The digital observatory facilitates data collection, access, and analysis.
Our initial release includes several datasets, code used to collect the data, and some simple analysis examples. Details are provided on the project page as well as our public Github repository. We will continue adding data, code, analysis, documentation, and more. We also welcome collaborators, pull-requests, and other contributions to the project.
What’s the goal for this project?
Our hope is that the public datasets and freely licensed tools, techniques, and knowledge created through the digital observatory will allow researchers, practitioners, and public health officials to more efficiently gather, analyze, understand, and act to improve these crucial sources of information during crises. Ultimately this will support ongoing responses to COVID-19 and contribute to future preparedness to respond to crisis events through analyses conducted after the fact.
How do I get access to the digital observatory?
The digital observatory data, code, and other resources will exist in a few locations, all linked from the project homepage. The data we collect, parse, and publish lives at covid19.communitydata.org/datasets. The code to collect, parse, and output those datasets lives in our Github repository, which also includes some scripts for getting started with analysis. We will integrate additional data and data collection resources from Pushshift and adjacent projects as we go. For more information, please check out the project page.
Stay up to date!
To receive updates on the digital observatory, please subscribe to our low traffic announcement mailing list. You will be the first to know about new datasets and other resources (and we won’t use or distribute addresses for any other reason).
This article is a reposted article from Doug Parry’s article in the UW iSchool News Website. The project is being driven by Stefania Druga who is part of the Community Data Science learning team and Mako. Jason Yip is a group friend.
Partners in the AI Literacy Project funded by Jacobs Fellowship (from top left to right): Jason Yip – Assistant Professor iSchool University of Washington, Stefania Druga – Doctoral Student iSchool University of Washington, Benjamin Mako Hill – Assistant Professor Department of Communications University of Washington, Indra Kubicek – CFO at Kids Code Jeunesse, David Moinina Sengeh – Minister Of Basic and Senior Secondary Education at Government of Sierra Leone, Kate Arthur – Founder & CEO at Kids Code Jeunesse, Michael Preston – co-founder CSforALL & Executive Director of Joan Ganz Cooney Center at Sesame Workshop.
A decade ago, teaching kids to code might have seemed far-fetched to some, but now coding curriculum is being widely adopted across the country. Recently researchers have turned their eye to the next wave of technology: artificial intelligence. As AI makes a growing impact on our lives, can kids benefit from learning how it works?
A three-year, $150,000 award from the Jacobs Foundation Research Fellowship Program will help answer that question. The fellowship awarded to Jason Yip, an assistant professor at the University of Washington Information School, will allow a team of researchers to investigate ways to educate kids about AI.
Stefania Druga, a first-year Ph.D. student advised by Yip , is among the researchers spearheading the effort. Druga came to the iSchool after earning her master’s at the Massachusetts Institute of Technology, where she launched Cognimates, a platform that teaches children how to train AI models and interact with them.
Druga’s desire to take Cognimates to the next level brought her to the University of Washington Information School and to her advisor, Yip, whose KidsTeam UW works with children to design technology. KidsTeam treats children as equal partners in the design process, ensuring the technology meets their needs — an approach known as co-design.
At MIT, “I realized there was only so far we could go,” Druga said. “In order for us to imagine what the future interfaces of AI learning for kids would look like, we need to have this longer-term relationship and partnership with kids, and co-design with kids, which is something Jason and the team here have done very well.”
Built on the widely used Scratch programming language, Cognimates is an open-source platform that gives kids the tools to teach computers how to recognize images and text and play games. Druga hopes the next iteration will help children truly understand the concepts behind AI — what is the robot “thinking” and who taught it to think that way? Even if they don’t grow up to be programmers or software engineers, the generation of “AI natives” will need to understand how technology works in order to be critical users.
“It matters as a new literacy,” Druga said, “especially for new generations who are growing up with technologies that become so embedded in things we use on a regular basis.”
Over the course of the fellowship, the research team will work with international partners to develop an AI literacy educational platform and curriculum in multiple languages for use in different settings, in both more- and less-developed parts of the world.
For Yip, the project brings the work of his Ph.D. student together with his work with KidsTeam with other recent research he has conducted on how families interact with AI.
“For me, it’s a proud moment when an advisee has a really cool vision that we can build together as a team,” Yip said. “This is a nice intersection of all of us coming together and thinking about what families need to understand artificial intelligence.”
The Jacobs Foundation fellowship program is open to early- and mid-career researchers from all scholarly disciplines around the world whose work contributes to the development and living conditions of children and youth. It’s highly competitive, with 10-15 fellowships chosen from hundreds of submissions each year.
If you are interested to get involved with this project or support in any way you may contact us at cognimates[a]gmail.com.
In May 2019, we were invited to give short remarks on the impact of Janet Fulk and Peter Monge at the International Communication Association‘s annual meeting as part of a session called “Igniting a TON (Technology, Organizing, and Networks) of Insights: Recognizing the Contributions of Janet Fulk and Peter Monge in Shaping the Future of Communication Research.”
Youtube: Mako Hill @ Janet Fulk and Peter Monge Celebration at ICA 2019
Mako Hill gave a four-minute talk on Janet and Peter’s impact to the work of the Community Data Science Collective. Mako unpacked some of the cryptic acronyms on the CDSC-UW lab’s whiteboard as well as explaining that our group has a home in the academic field of communication, in no small part, because of the pioneering scholarship of Janet and Peter. You can view the talk in WebM or on Youtube.
Do online communities compete with each other over resources or niches? Do they co-evolve in symbiotic or even parasitic relationships? What insights can we gain by applying ecological models of collective behavior to the study of collaborative online groups?
A colorful pisaster ochraceus (a.k.a., pisaster), a sea star species whose presence or absence can radically alter the ecology of an intertidal community. Our research will adapt theories created to explain the population dymamics of organisms like the pisaster in the context of online communities and human organizations (photo: Multi-Agency Rocky Intertidal Network).
We are delighted to announce that a Community Data Science Collective (CDSC) team led by Nate TeBlunthuis and Jeremy Foote has just started work on a three-year grant from the U.S. National Science Foundation to study the ecological dynamics of online communities! Aaron Shaw and Benjamin Mako Hill are principal investigators for the grant.
The projects supported by the award will extend the study of peer production and online communities by analyzing how aspects of communities’ environments impact their growth, patterns of participation, and survival. The work draws on recent research on various biological systems, organizational ecology, and human computer interaction (HCI). In general, we adapt these approaches to inform quantitative and computational analysis of populations of peer production communities and other online organizations.
As a major goal, we want to explain the conditions under which certain ecological dynamics emerge versus when they do not. For example, prior work has suggested that communities interact in ways that are both competitive and mutalistic. But what leads two communities to become competitors and others to benefit each other? We aim to understand when these patterns to arise. We are also interested in how community leaders might pursue effective strategies for survival given circumstances in the surrounding environment.
The grant promises to support a number of projects within the CDSC. Nate and Jeremy led the proposal writing as well as two key pilot studies that informed the development of the proposal. Other group members are now involved in planning and developing multiple studies under the grant.
The grant was awarded by the NSF Cyber-Human Systems (CHS) program within the Directorate for Information and Intellligent Systems (IIS) and the award is shared by Northwestern and the University of Washington (award numbers IIS-1910202 and IIS-1908850).
We’ve published the description of the proposal that we submitted to the NSF, although some details will shift as we carry out the project. The best place to stay up-to-date about the work will be to follow [the CDSC Twitter account (@ComDataSci)or the CDSC blog.
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 Aaron, Mako, Sayamindu, and Jeremy 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.
Members of the CDSC at a group retreat in Evanston, Illinois in September 2019. Clockwise from far right is: Salt, Jackie, Floor, Sejal, Nick, Kaylea, Sohyeon, Aaron, Nate, Jeremy, Mako, Jim, Charlie, and Regina. Sayamindu and Sneha are will us in spirit!
What are these different Ph.D. programs? Why would I choose one over the other?
The group currently includes four faculty principal investigators (PIs): 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). The 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 unique interests. The reasons you might choose to apply to one Ph.D. program or to work with a specific faculty member 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!
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 most of Mako’s students are in the Department of Communication, he also advises students in the Department of Computer Science and Engineering and HCDE—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 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 pre-certified in all the ways or 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.
The conference marks the official publication of four papers by collective students and faculty. All four papers were published in the journal Proceedings of the ACM on Human-Computer Interaction: CSCW.
Information on the talks as well as links to the papers are available here (CSCW members are listed in italics):
Mon, Nov 11 14:30 – 16:00: A Forensic Qualitative Analysis of Contributions to Wikipedia from Anonymity-Seeking Users by Kaylea Champion (UW), Nora McDonald (Drexel), Stephanie E Bankes (Drexel), Joseph Zhang (Drexel), Rachel Greenstadt (NYU), Andrea Forte (Drexel), Benjamin Mako Hill (UW). Kaylea will present! [Paper]
Mon, Nov 11 14:30 – 16:00: Wikipedia and Wiki Research
Salt, Kaylea, Charlie, Regina, and Kaylea will all be at the conference as will affiliate Andrés Monroy-Hernández and tons of our social computing friends. Please come and say “Hello” to any of us, introduce yourself if you don’t already know us, and pick up a CDSC sticker!
Why learning some statistics and some data visualization matters. (Gif from Matejka and Fitzmaurice, “Same stats, different graphs”, CHI 2017: https://dl.acm.org/citation.cfm?id=3025912)
I taught a graduate-level introduction to applied statistics and statistical computing this past Spring. The course design iterated on a class Mako developed in 2017. Very nearly all of the course materials are available open access through the Community Data Science Collective wiki and I wanted to make sure to share them more widely with this post. I’ve also been reflecting a bit on how the course went and thought I’d share those thoughts here in case anyone wants to adopt the course in the future.
First off, the course uses the OpenIntro Statistics (3rd edition) textbook as the core of the course readings and assignments. If you’re not familiar with OpenIntro and you want to learn or teach applied statistics from a general, social scientific perspective, you should check it out! All of the data, code, and LaTeX used to produce the textbook is licensed freely for reuse and the site also hosts video lectures, lecture notes, homework assignments, a discussion forum and more.
Alongside the OpenIntro materials, I worked together with Jeremy Foote (who was the TA for the course before he left to be new faculty at Purdue) to develop a bunch of tutorials in RMarkdown to help students complete the problem set assignments. We also posted worked solutions to the problem sets (also in RMarkdown). These replicated and expanded on screencasts Mako had recorded for his course.
The classroom sessions focused on discussion and problem solving. Basically, students came to each session knowing that I expected them to have completed the problem sets. I then did my best to answer any questions people had and assigned individuals (in some cases using a randomization script in R to pick names!) to summarize their solutions and approaches to specific problems that seemed important to cover.
It was my first time teaching a course like this and I had a few reflections after completing the quarter and reading through the feedback from students.
A major challenge for a course like this is pitching the material to an appropriate level given that students (in the MTS and TSB programs here at Northwestern at least) arrive with such varied knowledge of the subject matter. I think I did okay on this front in some ways and not in others. It was especially challenging given the semi-flipped classroom approach.
In some weeks, there was just too much material to cover in adequate depth. In some others, I was insufficiently organized and concise to cover everything. Whatever the case, I would cut back a bit next time. (I’ve noticed that this is a common issue for me the first time I teach any class, but I still struggle to correct it.)
Whatever challenges and failures I may have introduced in the design or instruction of the course, the students produced a bunch of highly original and engaging final projects. I’m optimistic that some of these projects will wind up as published work soon. Nothing like brilliant, motivated students to help the professor feel better about his own shortcomings!
Nearly all of the course materials are available on the CDSC wiki. The exceptions are a few of the readings and supplementary materials that I didn’t have the rights or desire to post on the public web. If you’re looking for any of that, feel free to send me an email and I can see if it’s appropriate to share.
Also, OpenIntro just came out with the fourth edition of their statistics textbook! I haven’t had a chance to check it out yet, but I’m eager to see what kinds of changes they introduced.
