Community Data Science Collective

Introducing new technology into a work place is often disruptive, but what if your work was also completely mediated by technology? This is exactly the case for the teams of volunteer moderators who work to regulate content and protect online communities from harm. What happens when the social media platforms these communities rely on change completely? How do moderation teams overcome the challenges caused by new technological environments? How do they do so while managing a “brand new” community with tens of thousands of users?

For a new study that will be published in CSCW in November, we interviewed 14 moderators of 8 “subreddit” communities from the social media aggregation and discussion platform Reddit to answer these questions. We chose these communities because each community had recently adopted the real-time chat platform Discord to support real-time chat in their community. This expansion into Discord introduced a range of challenges—especially for the moderation teams of large communities.

We found that moderation teams of large communities improvised their own creative solutions to challenges they faced by building bots on top of Discord’s API. This was not too shocking given that APIs and bots are frequently cited as tools that allow innovation and experimentation when scaling up digital work. What did surprise us, however, was how important moderators’ past experiences were in guiding the way they used bots. In the largest communities that faced the biggest challenges, moderators relied on bots to reproduce the tools they had used on Reddit. The moderators would often go so far as to give their bots the names of moderator tools available on Reddit. Our findings suggest that support for user-driven innovation is important not only in that it allows users to explore new technological possibilities but also in that it allows users to mine their past experiences to introduce old systems into new environments.

What Challenges Emerged in Discord?

Discord’s text channels allow for more natural, in the moment conversations compared to Reddit. In Discord, this social aspect also made moderation work much more difficult. One moderator explained:

“It’s kind of rough because if you miss it, it’s really hard to go back to something that happened eight hours ago and the conversation moved on and be like ‘hey, don’t do that.’ ”

Moderators we spoke to found that the work of managing their communities was made even more difficult by their community’s size:

On the day to day of running 65,000 people, it’s literally like running a small city…We have people that are actively online and chatting that are larger than a city…So it’s like, that’s a lot to actually keep track of and run and manage.”

The moderators of large communities repeatedly told us that the tools provided to moderators on Discord were insufficient. For example, they pointed out tools like Discord’s Audit Log was inadequate for keeping track of the tens of thousands of members of their communities. Discord also lacks automated moderation tools like the Reddit’s Automoderator and Modmail leaving moderators on Discord with few tools to scale their work and manage communications with community members. 

How Did Moderation Teams Overcome These Challenges?

The moderation teams we talked with adapted to these challenges through innovative uses of Discord’s API toolkit. Like many social media platforms, Discord offers a public API where users can develop apps that interact with the platform through a Discord “bot.” We found that these bots play a critical role in helping moderation teams manage Discord communities with large populations.

Guided by their experience with using tools like Automoderator on Reddit, moderators working on Discord built bots with similar functionality to solve the problems associated with scaled content and Discord’s fast-paced chat affordances. This bots would search for regular expressions and URLs that go against the community’s rules:

“It makes it so that rather than having to watch every single channel all of the time for this sort of thing or rely on users to tell us when someone is basically running amuck, posting derogatory terms and terrible things that Discord wouldn’t catch itself…so it makes it that we don’t have to watch every channel.”

Bots were also used to replace Discord’s Audit Log feature with what moderators referred to often as “Mod logs”—another term borrowed from Reddit. Moderators will send commands to a bot like “!warn username” to store information such as when a member of their community has been warned for breaking a rule and automatically store this information in a private text channel in Discord. This information helps organize information about community members, and it can be instantly recalled with another command to the bot to help inform future moderation actions against other community members.

Finally, moderators also used Discord’s API to develop bots that functioned virtually identically to Reddit’s Modmail tool. Moderators are limited in their availability to answer questions from members of their community, but tools like the “Modmail” helps moderation teams manage this problem by mediating communication to community members with a bot:

“So instead of having somebody DM a moderator specifically and then having to talk…indirectly with the team, a [text] channel is made for that specific question and everybody can see that and comment on that. And then whoever’s online responds to the community member through the bot, but everybody else is able to see what is being responded.”

The tools created with Discord’s API — customizable automated content moderation, Mod logs, and a Modmail system — all resembled moderation tools on Reddit. They even bear their names! Over and over, we found that moderation teams essentially created and used bots to transform aspects of Discord, like text channels into Mod logs and Mod Mail, to resemble the same tools they were using to moderate their communities on Reddit. 

What Does This Mean for Online Communities?

We think that the experience of moderators we interviewed points to a potentially important underlooked source of value for groups navigating technological change: the potent combination of users’ past experience combined with their ability to redesign and reconfigure their technological environments. Our work suggests the value of innovation platforms like APIs and bots is not only that they allow the discovery of “new” things. Our work suggests that these systems value also flows from the fact that they allow the re-creation of the the things that communities already know can solve their problems and that they already know how to use.

Our work has several more specific takeaways as well. For moderators and community leaders:

• Leaders of online communities planning to add an additional platform to host more discussion and social interactions for their community members should consider platforms with public APIs like Discord. You may run into challenges that the platform’s default tools are ineffective at solving, but public APIs allow for users to write software to solve their own problems.

For designers of online communities:

• Designers of social media applications and platforms that host online communities should consider the effects of community growth and large population sizes on the work of moderation teams, who are often unpaid volunteers with limited time and resources to manage their communities.
• Designers should also support a robust, public API for these applications and platforms. As our findings show, not every feature may be imagined in the creation of these platforms, but with a public API, users can drive the creation of custom solutions to unforeseen design problems. Our work suggests that these may be drawn from users unique knowledge of their problems as well as from their knowledge of existing solutions.

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Both this blog post and the paper it describes are collaborative work by Charles Kiene, Jialun “Aaron” Jiang, and Benjamin Mako Hill. For more details, check out the full 23 page paper. The work will be presented in Austin, Texas at the ACM Conference on Computer-supported Cooperative Work and Social Computing (CSCW’19) in November 2019. The work was supported by the National Science Foundation (awards IIS-1617129 and IIS-1617468). If you have questions or comments about this study, email Charles Kiene at ckiene [at] uw [dot] edu.

Paper Citation: Kiene, Charles, Jialun “Aaron” Jiang, and Benjamin Mako Hill. 2019. “Technological Frames and User Innovation: Exploring Technological Change in Community Moderation Teams.” Proceedings of the ACM: Human-Computer Interaction 3 (CSCW): 44:1-44:23.

Earlier this year, a team led by Kaylea Champion were announced as recipients of a generous grant from the Ford and Sloan Foundations to support research into into peer produced software infrastructure. Now that the project is moving forward in earnest, we’re thrilled to tell you about it.

The project is motivated by the fact that peer production communities have produced awesome free (both as in freedom and beer) resources—sites like Wikipedia that gather the world’s knowledge, and software like Linux that enables innovation, connection, commerce, and discovery. Over the last two decades, these resources have become key elements of public digital infrastructure that many of us rely on every day. However, some pieces of digital infrastructure we rely on most remain relatively under-resourced—as security vulnerabilities like Heartbleed in OpenSSL reveal. The grant from Ford and Sloan aims will support a research effort to understand how and why some software packages that are heavily used receive relatively little community support and maintenance.

We’re tackling this challenge by seeking to measure and model patterns of usage, contribution, and quality in a population of free software projects. We’ll then try to identify causes and potential solutions to the challenges of relative underproduction. Throughout, we’ll draw on both insight from the research community and on-the-ground observations from developers and community managers. We aim to create practical guidance that communities and software developers can actually use as well as novel research contributions. Underproduction is, appropriately enough, a challenge that has not gotten much attention from researchers previously, so we’re excited to work on it.

Although Kaylea Champion is leading the project, the team working on the project includes Benjamin Mako Hill, Aaron Shaw, and collective affiliate Morten Warncke-Wang who did pioneering work on underproduction in Wikipedia.

Jeremy Foote, Nate TeBlunthuis,  Wm Salt Hale, and Mako Hill will all be in Washington DC this week for the  International Communication Association’s 2019 annual meeting.

In particular, we be presenting a new paper from the group led by Sneha Narayan titled “All Talk: How Increasing Interpersonal Communication on Wikis May Not Enhance Productivity.” The talk will be on Monday, May 27 in a session from 9:30 to 10:45 in Washington Hilton LL, Holmead as part of a session organized by the ICA Computational Methods section on “Computational Approaches to Health Communication.”

Additionally, Nate is co-organizing a pre-conference at ICA on “Expanding Computational Communication: Towards a Pipeline for Graduate Students and Early Career Scholars” along with Josephine Lukito (UW Madison) and Frederic Hopp (UC Santa Barbara). The pre-conference will be held at American University on Friday May 24th. As part of that workshop, Nate and Jeremy will be giving a presentation on approaches to the study organizational communication that use computational methods.

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

Informal online learning communities are one of the most exciting and successful ways to engage young people in technology. As the most successful example of the approach, over 40 million children from around the world have created accounts on the Scratch online community where they learn to code by creating interactive art, games, and stories. However, despite its enormous reach and its focus on inclusiveness, participation in Scratch is not as broad as one would hope. For example, reflecting a trend in the broader computing community, more boys have signed up on the Scratch website than girls.

In a recently published paper, I worked with several colleagues from the Community Data Science Collective to unpack the dynamics of unequal participation by gender in Scratch by looking at whether Scratch users choose to share the projects they create. Our analysis took advantage of the fact that less than a third of projects created in Scratch are ever shared publicly. By never sharing, creators never open themselves to the benefits associated with interaction, feedback, socialization, and learning—all things that research has shown participation in Scratch can support.

Overall, we found that boys on Scratch share their projects at a slightly higher rate than girls. Digging deeper, we found that this overall average hid an important dynamic that emerged over time. The graph below shows the proportion of Scratch projects shared for male and female Scratch users’ 1st created projects, 2nd created projects, 3rd created projects, and so on. It reflects the fact that although girls share less often initially, this trend flips over time. Experienced girls share much more than often than boys!

We unpacked this dynamic using a series of statistical models estimated using data from over 5 million projects by over a million Scratch users. This set of analyses echoed our earlier preliminary finding—while girls were less likely to share initially, more experienced girls shared projects at consistently higher rates than boys. We further found that initial differences in sharing between boys and girls could be explained by controlling for differences in project complexity and in the social connectedness of the project creator.

Another surprising finding is that users who had received more positive peer feedback, at least as measured by receipt of “love its” (similar to “likes” on Facebook), were less likely to share their subsequent projects than users who had received less. This relation was especially strong for boys and for more experienced Scratch users. We speculate that this could be due to a phenomenon known in the music industry as “sophomore album syndrome” or “second album syndrome”—a term used to describe a musician who has had a successful first album but struggles to produce a second because of increased pressure and expectations caused by their previous success

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This blog post and the paper are collaborative work with Benjamin Mako Hill and Sayamindu Dasgupta. You can find more details about our methodology and results in the text of our paper, “Gender, Feedback, and Learners’ Decisions to Share Their Creative Computing Projects” which is freely available and published open access in the Proceedings of the ACM on Human-Computer Interaction 2 (CSCW): 54:1-54:23.

Online anonymity often gets a bad rap and complaints about antisocial behavior from anonymous Internet users are as old as the Internet itself. On the other hand, research has shown that many Internet users seek out anonymity to protect their privacy while contributing things of value. Should people seeking to contribute to open collaboration projects like open source software and citizen science projects be required to give up identifying information in order to participate?

We conducted a two-part study to better understand how open collaboration projects balance the threats of bad behavior with the goal of respecting contributors’ expectations of privacy. First, we interviewed eleven people from five different open collaboration “service providers” to understand what threats they perceive to their projects’ mission and how these threats shape privacy and security decisions when it comes to anonymous contributions. Second, we analyzed discussions about anonymous contributors on publicly available logs of the English language Wikipedia mailing list from 2010 to 2017.

In the interview study, we identified three themes that pervaded discussions of perceived threats. These included threats to:

1. community norms, such as harrassment;
2. sustaining participation, such as loss of or failure to attract volunteers; and
3. contribution quality, low-quality contributions drain community resources.

We found that open collaboration providers were most concerned with lowering barriers to participation to attract new contributors. This makes sense given that newbies are the lifeblood of open collaboration communities. We also found that service providers thought of anonymous contributions as a way of offering low barriers to participation, not as a way of helping contributors manage their privacy. They imagined that anonymous contributors who wanted to remain in the community would eventually become full participants by registering for an account and creating an identity on the site. This assumption was evident in policies and technical features of collaboration platforms that barred anonymous contributors from participating in discussions, receiving customized suggestions, or from contributing at all in some circumstances. In our second study of the English language Wikipedia public email listserv, we discovered that the perspectives we encountered in interviews also dominated discussions of anonymity on Wikipedia. In both studies, we found that anonymous contributors were seen as “second-class citizens.”

This is not the way anonymous contributors see themselves. In a study we published two years ago, we interviewed people who sought out privacy when contributing to open collaboration projects. Our subjects expressed fears like being doxed, shot at, losing their job, or harassed. Some were worried about doing or viewing things online that violated censorship laws in their home country. The difference between the way that anonymity seekers see themselves and the way they are seen by service providers was striking.

One cause of this divergence in perceptions around anonymous contributors uncovered by our new paper is that people who seek out anonymity are not able to participate fully in the process of discussing and articulating norms and policies around anonymous contribution. People whose anonymity needs means they cannot participate in general cannot participate in the discussions that determine who can participate.

We conclude our paper with the observation that, although social norms have played an important role in HCI research, relying on them as a yardstick for measuring privacy expectations may leave out important minority experiences whose privacy concerns keep them from participating in the first place. In online communities like open collaboration projects, social norms may best reflect the most privileged and central users of a system while ignoring the most vulnerable

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Both this blog post and the paper, Privacy, Anonymity, and Perceived Risk in Open Collaboration: A Study of Service Providers, was written by Nora McDonald, Benjamin Mako Hill, Rachel Greenstadt, and Andrea Forte and will be published in the Proceedings of the 2019 ACM CHI Conference on Human Factors in Computing Systems next week. The paper will be presented at the CHI conference in Glasgow, UK on Wednesday May 8, 2019. The work was supported by the National Science Foundation (awards CNS-1703736 and CNS-1703049).

In exciting news, Benjamin Mako Hill was just announced as a winner of a 2019 Research Symbiont Award.  Mako received the second annual General Symbiosis Award which “is given to a scientist working in any field who has shared data beyond the expectations of their field.” The award was announced at a ceremony in Hawaii at the Pacific Symposium in Biocomputing.

The award presentation called out Mako’s work on the preparation of the Scratch research dataset that includes the first five years of longitudinal data from the Scratch online community. Andrés Monroy-Hernández worked with Mako on that project. Mako’s nomination also mentioned his research groups’ commitment to the production of replication datasets as well as his work with Aaron Shaw on datasets of redirects and page protection from Wikipedia. Mako was asked to talk about this work in his a short video he recorded that was shown at the award ceremony.

The Research Symbionts Awards are given annually to recognize “symbiosis” in the form of data sharing. They are a companion award to the Research Parasite Awards which recognize superb examples of secondary data reuse. The award includes money to travel to the Pacific Symposium Computing (unfortunately, Mako wasn’t able to take advantage of this!) as well the plush fish with parasitic lamprey shown here.

In addition to the award given to Mako, Dr. Leonardo Collado-Torres was announced as the recipient of the health-specific Early Career Symobiont award for his work on Recount2.

I’ve heard a surprising “fact” repeated in the CHI and CSCW communities that receiving a best paper award at a conference is uncorrelated with future citations. Although it’s surprising and counterintuitive, it’s a nice thing to think about when you don’t get an award and its a nice thing to say to others when you do. I’ve thought it and said it myself.

It also seems to be untrue. When I tried to check the “fact” recently, I found a body of evidence that suggests that computing papers that receive best paper awards are, in fact, cited more often than papers that do not.

The source of the original “fact” seems to be a CHI 2009 study by Christoph Bartneck and Jun Hu titled “Scientometric Analysis of the CHI Proceedings.” Among many other things, the paper presents a null result for a test of a difference in the distribution of citations across best papers awardees, nominees, and a random sample of non-nominees.

Although the award analysis is only a small part of Bartneck and Hu’s paper, there have been at least two papers have have subsequently brought more attention, more data, and more sophisticated analyses to the question.  In 2015, the question was asked by Jaques Wainer, Michael Eckmann, and Anderson Rocha in their paper “Peer-Selected ‘Best Papers’—Are They Really That ‘Good’?“

Wainer et al. build two datasets: one of papers from 12 computer science conferences with citation data from Scopus and another papers from 17 different conferences with citation data from Google Scholar. Because of parametric concerns, Wainer et al. used a non-parametric rank-based technique to compare awardees to non-awardees.  Wainer et al. summarize their results as follows:

The probability that a best paper will receive more citations than a non best paper is 0.72 (95% CI = 0.66, 0.77) for the Scopus data, and 0.78 (95% CI = 0.74, 0.81) for the Scholar data. There are no significant changes in the probabilities for different years. Also, 51% of the best papers are among the top 10% most cited papers in each conference/year, and 64% of them are among the top 20% most cited.

The question was also recently explored in a different way by Danielle H. Lee in her paper on “Predictive power of conference‐related factors on citation rates of conference papers” published in June 2018.

Lee looked at 43,000 papers from 81 conferences and built a regression model to predict citations. Taking into an account a number of controls not considered in previous analyses, Lee finds that the marginal effect of receiving a best paper award on citations is positive, well-estimated, and large.

Why did Bartneck and Hu come to such a different conclusions than later work?

My first thought was that perhaps CHI is different than the rest of computing. However, when I looked at the data from Bartneck and Hu’s 2009 study—conveniently included as a figure in their original study—you can see that they did find a higher mean among the award recipients compared to both nominees and non-nominees. The entire distribution of citations among award winners appears to be pushed upwards. Although Bartneck and Hu found an effect, they did not find a statistically significant effect.

Given the more recent work by Wainer et al. and Lee, I’d be willing to venture that the original null finding was a function of the fact that citations is a very noisy measure—especially over a 2-5 post-publication period—and that the Bartneck and Hu dataset was small with only 12 awardees out of 152 papers total. This might have caused problems because the statistical test the authors used was an omnibus test for differences in a three-group sample that was imbalanced heavily toward the two groups (nominees and non-nominees) in which their appears to be little difference. My bet is that the paper’s conclusions on awards is simply an example of how a null effect is not evidence of a non-effect—especially in an underpowered dataset.

Of course, none of this means that award winning papers are better. Despite Wainer et al.’s claim that they are showing that award winning papers are “good,” none of the analyses presented can disentangle the signalling value of an award from differences in underlying paper quality. The packed rooms one routinely finds at best paper sessions at conferences suggest that at least some additional citations received by award winners might be caused by extra exposure caused by the awards themselves. In the future, perhaps people can say something along these lines instead of repeating the “fact” of the non-relationship.

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This post was originally posted on Benjamin Mako Hill’s blog Copyrighteous.

This post was co-authored by Benjamin Mako Hill and Aaron Shaw. We wrote it following a conversation with the CSCW 2018 papers chairs. At their encouragement, we put together this proposal that we plan to bring to the CSCW town hall meeting. Thanks to Karrie Karahalios, Airi Lampinen, Geraldine Fitzpatrick, and Andrés Monroy-Hernández for engaging in the conversation with us and for facilitating the participation of the CSCW community.

False discovery in empirical research

There is growing evidence that an enormous portion of published quantitative research is wrong. In fields where recognition of “false discovery” has prompted systematic re-examinations of published findings, it has led to a replication crisis. For example, a systematic attempt to reproduce influential results in social psychology failed to replicate a majority of them. Another attempt focused on social research in top general science journals and failed to replicate more than a third and found that the size of effects were, on average, overstated by a factor of two.

Quantitative methodologists argue that the high rates of false discovery are, among other reasons, a function of common research practices carried out in good faith. Such practices include accidental or intentional p-hacking where researchers try variations of their analysis until they find significant results; a garden of forking paths where researcher decisions lead to a vast understatement of the number of true “researcher degrees of freedom” in their research designs; the file-drawer problem which leads only statistically significant results to be published; and underpowered studies, which make it so that only overstated effect sizes can be detected.

To the degree that much of CSCW and HCI use the same research methods and approaches as these other social scientific fields, there is every reason to believe that these issues extend to social computing research. Of course, given that replication is exceedingly rare in HCI, HCI researchers will rarely even find out that a result is wrong.

To date, no comprehensive set of solutions to these issues exists. However, scholarly communities can take steps to reduce the threat of false discovery. One set of approaches to doing so involves the introduction of changes to the way quantitative studies are planned, executed, and reviewed. We want to encourage the CSCW community to consider supporting some of these practices.

Among the approaches developed and adopted in other research communities, several involve breaking up research into two distinct stages: a first stage in which research designs are planned, articulated, and recorded; and a second stage in which results are computed following the procedures in the recorded design (documenting any changes). This stage-based process ensures that designs cannot shift in ways that shape findings without some clear acknowledgement that such a shift has occurred. When changes happen, adjustments can sometimes be made in the computation of statistical tests. Readers and reviewers of the work can also have greater awareness of the degree to which the statistical tests accurately reflect the analysis procedures or not and adjust their confidence in the findings accordingly.

Versions of these stage-based research designs were first developed in biomedical randomized controlled trials (RCTs) and are extremely widespread in that domain. For example, pre-registration of research designs is now mandatory for NIH funded RCTs and several journals are reviewing and accepting or rejecting studies based on pre-registered designs before results are known.

A proposal for CSCW

In order to address the challenges posed by false discovery, CSCW could adopt a variety of approaches from other fields that have already begun to do so. These approaches entail more or less radical shifts to the ways in which CSCW research gets done, reviewed, and published.

As a starting point, we want to initiate discussion around one specific proposal that could be suitable for a number of social computing studies and would require relatively little in the way of changes to the research and reviewing processes used in our community.

Drawing from a series of methodological pieces in the social sciences ([1], [2], [3]), we propose a method based on split-sample designs that would be entirely optional for CSCW authors at the time of submission.

Essentially, authors who chose to do so could submit papers which were written—and which will be reviewed and revised—based on one portion of their dataset with the understanding that the paper would be published using identical analytic methods also applied to a second, previously un-analyzed portion of the dataset. Authors submitting under this framework would choose to have their papers reviewed, revised and resubmitted, and accepted or rejected based on the quality of the research questions, framing, design, execution, and significance of the study overall. The decision would not be based on the statistical significance of final analysis results.

The idea follows from the statistical technique of “cross validation,” in which an analysis is developed on one subset of data (usually called the “training set”) and then replicated on at least one other subset (the “test set”).

To conduct a project using this basic approach, a researcher would:

• Randomly partition their full dataset into two (or more) pieces.
• Design, refine, and complete their analysis using only one piece identified as the training sample.
• Undergo the CSCW review process using the results from this analysis of the training sample.
• If the submission receives a decision of “Revise and Resubmit,” authors would then make changes to the analysis of the training sample as requested by ACs and reviewers in the current normal way.
• If the paper is accepted for publication, the authors would then (and only then) run the final version of the analysis using another piece of their data identified as the test sample and publish those results in the paper.
• We expect that authors would also publish the training set results used during review in the online supplement to their paper uploaded to the ACM Digital Library.
• Like any other part part of a paper’s methodology, the split sample procedure would be documented in appropriate parts of the paper.

We are unaware of prior work in social computing that has applied this process. Researchers in data mining, machine learning, and related fields of computer science use cross-validation all the time, they do so differently in order to solve distinct problems (typically related to model overfitting).

The main benefits of this approach (discussed in much more depth in the references at the beginning of this section) would be:

• Heightened reliability and reproducibility of the analysis.
• Reduced risk that findings reflect spurious relationships, p-hacking, researcher or reviewer degrees of freedom, or other pitfalls of statistical inference common in the analysis of behavioral data—i.e., protection against false discovery.
• A procedural guarantee that the results do not determine the publication (or not) of the work—i.e., protection against publication bias.

The most salient risk from the approach is that results might change when authors run the final analysis on the test set.  In the absence of p-hacking and similar issues, such changes will usually be small and will mostly impact the magnitude of effects estimates and their associated standard errors. However, some changes might be more dramatic. Dealing with changes of this sort would be harder for authors and reviewers and would potentially involve something along the lines of the shepherding that some papers receive now.

Let’s talk it over!

This blog post is meant to spark a wider discussion. We hope this can happen during CSCW this year and beyond. We believe the procedure we have proposed would enhance the reliability of our work and is workable in CSCW because it involves narrow changes to the way that quantitative CSCW research and reviewing is usually conducted. We also believe this procedure would serve the long term interests of the HCI and social computing research community. CSCW is a leader in building better models of scientific publishing within HCI through the R&R process, eliminated page limits, the move to PACM, and more. We would like to extend this spirit to issues of reproducibility and publication bias. We are eager to discuss our proposal and welcome suggestions for changes.

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