The Northwestern University branch of the Community Data Science Collective (CDSC) is hiring research assistants. CDSC is an interdisciplinary research group made of up of faculty and students at multiple institutions, including Northwestern University, Purdue University, and the University of Washington. We’re social and computer scientists studying online communities such as Wikipedia, Reddit, Scratch, and more.
Recent work by the group includes studies of participation inequalities in online communities and the gig economy, comparisons of different online community rules and norms, and evaluations of design changes deployed across thousands of sites. More examples and information can be found on our list of publications and our research blog (you’re probably reading our blog right now).
This posting is specifically to work on some projects through the Northwestern University part of the CDSC. Northwestern Research Assistants will contribute to data collection, analysis, documentation, and administration on one (or more) of the group’s ongoing projects. Some research projects you might help with include:
A study of rules across the five largest language editions of Wikipedia.
A systematic literature review on the gig economy.
Interviews with contributors to small, niche subreddit communities.
A large-scale analysis of the relationships between communities.
Successful applicants will have an interest in online communities, social science or social computing research, and the ability to balance collaborative and independent work. No specialized skills are required and we will adapt work assignments and training to the skills and interests of the person(s) hired. Relevant skills might include: coursework, research, and/or familiarity with digital media, online communities, human computer interaction, social science research methods such as interviewing, applied statistics, and/or data science. Relevant software experience might include: R, Python, Git, Zotero, or LaTeX. Again, no prior experience or specialized skills are required.
Expected minimum time commitment is 10 hours per week through the remainder of the Winter quarter (late March) with the possibility of working additional hours and/or continuing into the Spring quarter (April-June). All work will be performed remotely.
Interested applicants should submit a resume (or CV) along with a short cover letter explaining your interest in the position and any relevant experience or skills. Applicants should indicate whether you would prefer to pursue this through Federal work-study, for course credit (most likely available only to current students at one of the institutions where CDSC affiliates work), or as a paid position (not Federal work-study). For paid positions, compensation will be $15 per hour. Some funding may be restricted to current undergraduate students (at any institution), which may impact hiring decisions.
Questions and/or applications should be sent to Professor Aaron Shaw. Work-study eligible Northwestern University students should indicate this in their cover letter. Applications will be reviewed by Professor Shaw and current CDSC-NU team members on a rolling basis and finalists will be contacted for an interview.
The CDSC strives to be an inclusive and accessible research community. We particularly welcome applications from members of groups historically underrepresented in computing and/or data sciences. Some of these positions funded through a U.S. National Science Foundation Research Experience for Undergraduates (REU) supplement to awards numbers: IIS-1910202 and IIS-1617468.
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.
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.
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.
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.
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.
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!
Although Wikipedia is the encyclopedia that anybody can edit, not all edits are welcome. Wikipedia is subject to a constant deluge of vandalism. Random people on the Internet are constantly “blanking” Wikipedia articles by deleting their content, replacing the text of articles with random characters, inserting outlandish claims or insults, and so on. Although volunteer editors and bots do an excellent job of quickly reverting the damage, the cost in terms of volunteer time is real.
Why do people spend their time and energy vandalizing web pages? For readers of Wikipedia that encounter a page that has been marred or replaced with nonsense or a slur—and especially for all the Wikipedia contributors who spend their time fighting back the tide of vandalism by checking and reverting bad edits and maintaining the bots and systems that keep order—it’s easy to dismiss vandals as incomprehensible sociopaths.
In a paper I just published in the ACM International Conference on Social Media and Society, I systematically analyzed a dataset of Wikipedia vandalism in an effort to identify different types of Wikipedia vandalism and to explain how each can been seen as “rational” from the point of view of the vandal.
Leveraging a dataset we created in some of our other work, the study used a random sample of contributions drawn from four groups that vary in the degree to the editors in question can be identified by others in Wikipedia: established users with accounts, users with accounts making their first edits, users without accounts, and users of the Tor privacy tool. Tor users were of particular interest to me because the use of Tor offers concrete evidence that a contributor is deliberately seeking privacy. I compared the frequency of vandalism in each group, developed an ontology to categorize it, and tested the relationship between group membership and different types of vandalism.
I found that the group that had engaged in the least effort in order to edit—users without accounts—were the most likely to vandalize. Although privacy-seeking Tor contributors were not the most likely to vandalize, vandalism from Tor-based contributors was less likely to be sociable, was more likely to be large scale (i.e. large blocks of text, such as by pasting in the same lines over and over), and more likely to express frustration with the Wikipedia community.
Thinking systematically about why different groups of users might engage in vandalism can help counter vandalism. Potential interventions might change not just the amount, but also the type, of vandalism a community will receive. Tools to detect vandalism may find that the patterns in each category allow for more accurate targeting. Ultimately, viewing vandals as more than irrational sociopaths opens potential avenues for dialogue.
Paper Citation: Kaylea Champion. 2020. “Characterizing Online Vandalism: A Rational Choice Perspective.” In International Conference on Social Media and Society (SMSociety’20). Association for Computing Machinery, New York, NY, USA, 47–57. https://doi.org/10.1145/3400806.3400813
Like everyone else, Internet users who protect their privacy by using the anonymous browsing software Tor are welcome to read Wikipedia. However, when Tor users try to contribute to the self-described “encyclopedia that anybody can edit,” they typically come face-to-face with a notice explaining that their participation is not welcome.
Our new paper—led by Chau Tran at NYU and authored by a group of researchers from the University of Washington, the Community Data Science Collective, Drexel, and New York University—was published and presented this week at the IEEE Symposium on Security & Privacy and provides insight into what Wikipedia might be missing out on by blocking Tor. By comparing contributions from Tor that slip past Wikipedia’s ban to edits made by other types of contributors, we find that Tor users make contributions to Wikipedia that are just as valuable as those made by new and unregistered Wikipedia editors. We also found that Tor users are more likely to engage with certain controversial topics.
To conduct our study, we first identified more than 11,000 Wikipedia edits made by Tor users who were able to bypass Wikipedia’s ban on contributions from Tor between 2007 and 2018. We then used a series of quantitative techniques to evaluate the quality of these contributions. We found that Tor users made contributions that were similar in quality to, and in some senses even better than, contributions made by other users without accounts and newcomers making their first edits.
We used a range of analytical techniques including direct parsing of article histories, manual inspections of article changes, and a machine learning platform called ORES to analyze contributions. We also used a machine learning technique called topic modeling to analyze Tor users’ areas of interest by checking their edits against clusters of keywords. We found that Tor-based editors are more likely than other users to focus on topics that may be considered controversial, such as politics, technology, and religion.
In a closely connected study led by Kaylea Champion and published several months ago in the Proceedings of the ACM on Human Computer Interaction (CSCW), we conducted a forensic qualitative analysis of contributions of the same dataset. Our results in that study are described in a separate blog post about that project and paint a complementary picture of Tor users engaged—in large part—in uncontroversial and quotidian types of editing behavior.
Across the two papers, our results are similar to other work that suggests that Tor users are very similar to other internet users. For example, one previous study has shown that Tor users frequently visit websites in the Alexa top one million.
Much of the discourse about anonymity online tends toward extreme claims backed up by very little in the way of empirical evidence or systematic study. Our work is a step toward remedying this gap and has implications for many websites that limit participation by users of anonymous browsing software like Tor. In the future, we hope to conduct similar systematic studies in contexts beyond Wikipedia.
In terms of Wikipedia’s own policy decisions about anonymous participation, we believe that our paper suggests that the benefits of a “pathway to legitimacy” for Tor contributors to Wikipedia might exceed the potential harm due to the value of their contributions. We are particularly excited about exploring ways to allow contributors from anonymity-seeking users under certain conditions: for example, requiring review prior to changes going live. Of course, these are questions for the Wikipedia community to decide but it’s a conversation that we hope our research can inform and that we look forward to participating in.
Paper Citation: Tran, Chau, Kaylea Champion, Andrea Forte, Benjamin Mako Hill, and Rachel Greenstadt. “Are Anonymity-Seekers Just like Everybody Else? An Analysis of Contributions to Wikipedia from Tor.” In 2020 IEEE Symposium on Security and Privacy (SP), 1:974–90. San Francisco, California: IEEE Computer Society, 2020. https://doi.org/10.1109/SP40000.2020.00053.
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.
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.
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.
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.
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.
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.