Introducing Computational Methods to Social Media Scientists

The ubiquity of large-scale data and improvements in computational hardware and algorithms have provided enabled researchers to apply computational approaches to the study of human behavior. One of the richest contexts for this kind of work is social media datasets like Facebook, Twitter, and Reddit.

We were invited by Jean BurgessAlice Marwick, and Thomas Poell to write a chapter about computational methods for the Sage Handbook of Social Media. Rather than simply listing what sorts of computational research has been done with social media data, we decided to use the chapter to both introduce a few computational methods and to use those methods in order to analyze the field of social media research.

A “hairball” diagram from the chapter illustrating how research on social media clusters into distinct citation network neighborhoods.

Explanations and Examples

In the chapter, we start by describing the process of obtaining data from web APIs and use as a case study our process for obtaining bibliographic data about social media publications from Elsevier’s Scopus API.  We follow this same strategy in discussing social network analysis, topic modeling, and prediction. For each, we discuss some of the benefits and drawbacks of the approach and then provide an example analysis using the bibliographic data.

We think that our analyses provide some interesting insight into the emerging field of social media research. For example, we found that social network analysis and computer science drove much of the early research, while recently consumer analysis and health research have become more prominent.

More importantly though, we hope that the chapter provides an accessible introduction to computational social science and encourages more social scientists to incorporate computational methods in their work, either by gaining computational skills themselves or by partnering with more technical colleagues. While there are dangers and downsides (some of which we discuss in the chapter), we see the use of computational tools as one of the most important and exciting developments in the social sciences.

Steal this paper!

One of the great benefits of computational methods is their transparency and their reproducibility. The entire process—from data collection to data processing to data analysis—can often be made accessible to others. This has both scientific benefits and pedagogical benefits.

To aid in the training of new computational social scientists, and as an example of the benefits of transparency, we worked to make our chapter pedagogically reproducible. We have created a permanent website for the chapter at https://communitydata.cc/social-media-chapter/ and uploaded all the code, data, and material we used to produce the paper itself to an archive in the Harvard Dataverse.

Through our website, you can download all of the raw data that we used to create the paper, together with code and instructions for how to obtain, clean, process, and analyze the data. Our website walks through what we have found to be an efficient and useful workflow for doing computational research on large datasets. This workflow even includes the paper itself, which is written using LaTeX + knitr. These tools let changes to data or code propagate through the entire workflow and be reflected automatically in the paper itself.

If you  use our chapter for teaching about computational methods—or if you find bugs or errors in our work—please let us know! We want this chapter to be a useful resource, will happily consider any changes, and have even created a git repository to help with managing these changes!

OpenSym 2017 Program Postmortem

The International Symposium on Open Collaboration (OpenSym, formerly WikiSym) is the premier academic venue exclusively focused on scholarly research into open collaboration. OpenSym is an ACM conference which means that, like conferences in computer science, it’s really more like a journal that gets published once a year than it is like most social science conferences. The “journal”, in this case, is called the Proceedings of the International Symposium on Open Collaboration and it consists of final copies of papers which are typically also presented at the conference. Like journal articles, papers that are published in the proceedings are not typically published elsewhere.

Along with Claudia Müller-Birn from the Freie Universtät Berlin, I served as the Program Chair for OpenSym 2017. For the social scientists reading this, the role of program chair is similar to being an editor for a journal. My job was not to organize keynotes or logistics at the conference—that is the job of the General Chair. Indeed, in the end I didn’t even attend the conference! Along with Claudia, my role as Program Chair was to recruit submissions, recruit reviewers, coordinate and manage the review process, make final decisions on papers, and ensure that everything makes it into the published proceedings in good shape.

In OpenSym 2017, we made several changes to the way the conference has been run:

  • In previous years, OpenSym had tracks on topics like free/open source software, wikis, open innovation, open education, and so on. In 2017, we used a single track model.
  • Because we eliminated tracks, we also eliminated track-level chairs. Instead, we appointed Associate Chairs or ACs.
  • We eliminated page limits and the distinction between full papers and notes.
  • We allowed authors to write rebuttals before reviews were finalized. Reviewers and ACs were allowed to modify their reviews and decisions based on rebuttals.
  • To assist in assigning papers to ACs and reviewers, we made extensive use of bidding. This means we had to recruit the pool of reviewers before papers were submitted.

Although each of these things have been tried in other conferences, or even piloted within individual tracks in OpenSym, all were new to OpenSym in general.

Overview

Statistics
Papers submitted 44
Papers accepted 20
Acceptance rate 45%
Posters submitted 2
Posters presented 9
Associate Chairs 8
PC Members 59
Authors 108
Author countries 20

The program was similar in size to the ones in the last 2-3 years in terms of the number of submissions. OpenSym is a small but mature and stable venue for research on open collaboration. This year was also similar, although slightly more competitive, in terms of the conference acceptance rate (45%—it had been slightly above 50% in previous years).

As in recent years, there were more posters presented than submitted because the PC found that some rejected work, although not ready to be published in the proceedings, was promising and advanced enough to be presented as a poster at the conference. Authors of posters submitted 4-page extended abstracts for their projects which were published in a “Companion to the Proceedings.”

Topics

Over the years, OpenSym has established a clear set of niches. Although we eliminated tracks, we asked authors to choose from a set of categories when submitting their work. These categories are similar to the tracks at OpenSym 2016. Interestingly, a number of authors selected more than one category. This would have led to difficult decisions in the old track-based system.

distribution of papers across topics with breakdown by accept/poster/reject

The figure above shows a breakdown of papers in terms of these categories as well as indicators of how many papers in each group were accepted. Papers in multiple categories are counted multiple times. Research on FLOSS and Wikimedia/Wikipedia continue to make up a sizable chunk of OpenSym’s submissions and publications. That said, these now make up a minority of total submissions. Although Wikipedia and Wikimedia research made up a smaller proportion of the submission pool, it was accepted at a higher rate. Also notable is the fact that 2017 saw an uptick in the number of papers on open innovation. I suspect this was due, at least in part, to work by the General Chair Lorraine Morgan’s involvement (she specializes in that area). Somewhat surprisingly to me, we had a number of submission about Bitcoin and blockchains. These are natural areas of growth for OpenSym but have never been a big part of work in our community in the past.

Scores and Reviews

As in previous years, review was single blind in that reviewers’ identities are hidden but authors identities are not. Each paper received between 3 and 4 reviews plus a metareview by the Associate Chair assigned to the paper. All papers received 3 reviews but ACs were encouraged to call in a 4th reviewer at any point in the process. In addition to the text of the reviews, we used a -3 to +3 scoring system where papers that are seen as borderline will be scored as 0. Reviewers scored papers using full-point increments.

scores for each paper submitted to opensym 2017: average, distribution, etc

The figure above shows scores for each paper submitted. The vertical grey lines reflect the distribution of scores where the minimum and maximum scores for each paper are the ends of the lines. The colored dots show the arithmetic mean for each score (unweighted by reviewer confidence). Colors show whether the papers were accepted, rejected, or presented as a poster. It’s important to keep in mind that two papers were submitted as posters.

Although Associate Chairs made the final decisions on a case-by-case basis, every paper that had an average score of less than 0 (the horizontal orange line) was rejected or presented as a poster and most (but not all) papers with positive average scores were accepted. Although a positive average score seemed to be a requirement for publication, negative individual scores weren’t necessary showstoppers. We accepted 6 papers with at least one negative score. We ultimately accepted 20 papers—45% of those submitted.

Rebuttals

This was the first time that OpenSym used a rebuttal or author response and we are thrilled with how it went. Although they were entirely optional, almost every team of authors used it! Authors of 40 of our 46 submissions (87%!) submitted rebuttals.

Lower Unchanged Higher
6 24 10

The table above shows how average scores changed after authors submitted rebuttals. The table shows that rebuttals’ effect was typically neutral or positive. Most average scores stayed the same but nearly two times as many average scores increased as decreased in the post-rebuttal period. We hope that this made the process feel more fair for authors and I feel, having read them all, that it led to improvements in the quality of final papers.

Page Lengths

In previous years, OpenSym followed most other venues in computer science by allowing submission of two kinds of papers: full papers which could be up to 10 pages long and short papers which could be up to 4. Following some other conferences, we eliminated page limits altogether. This is the text we used in the OpenSym 2017 CFP:

There is no minimum or maximum length for submitted papers. Rather, reviewers will be instructed to weigh the contribution of a paper relative to its length. Papers should report research thoroughly but succinctly: brevity is a virtue. A typical length of a “long research paper” is 10 pages (formerly the maximum length limit and the limit on OpenSym tracks), but may be shorter if the contribution can be described and supported in fewer pages— shorter, more focused papers (called “short research papers” previously) are encouraged and will be reviewed like any other paper. While we will review papers longer than 10 pages, the contribution must warrant the extra length. Reviewers will be instructed to reject papers whose length is incommensurate with the size of their contribution.

The following graph shows the distribution of page lengths across papers in our final program.

histogram of paper lengths for final accepted papersIn the end 3 of 20 published papers (15%) were over 10 pages. More surprisingly, 11 of the accepted papers (55%) were below the old 10-page limit. Fears that some have expressed that page limits are the only thing keeping OpenSym from publshing enormous rambling manuscripts seems to be unwarranted—at least so far.

Bidding

Although, I won’t post any analysis or graphs, bidding worked well. With only two exceptions, every single assigned review was to someone who had bid “yes” or “maybe” for the paper in question and the vast majority went to people that had bid “yes.” However, this comes with one major proviso: people that did not bid at all were marked as “maybe” for every single paper.

Given a reviewer pool whose diversity of expertise matches that in your pool of authors, bidding works fantastically. But everybody needs to bid. The only problems with reviewers we had were with people that had failed to bid. It might be reviewers who don’t bid are less committed to the conference, more overextended, more likely to drop things in general, etc. It might also be that reviewers who fail to bid get poor matches which cause them to become less interested, willing, or able to do their reviews well and on time.

Having used bidding twice as chair or track-chair, my sense is that bidding is a fantastic thing to incorporate into any conference review process. The major limitations are that you need to build a program committee (PC) before the conference (rather than finding the perfect reviewers for specific papers) and you have to find ways to incentivize or communicate the importance of getting your PC members to bid.

Conclusions

The final results were a fantastic collection of published papers. Of course, it couldn’t have been possible without the huge collection of conference chairs, associate chairs, program committee members, external reviewers, and staff supporters.

Although we tried quite a lot of new things, my sense is that nothing we changed made things worse and many changes made things smoother or better. Although I’m not directly involved in organizing OpenSym 2018, I am on the OpenSym steering committee. My sense is that most of the changes we made are going to be carried over this year.

Finally, it’s also been announced that OpenSym 2018 will be in Paris on August 22-24. The call for papers should be out soon and the OpenSym 2018 paper deadline has already been announced as March 15, 2018. You should consider submitting! I hope to see you in Paris!

This Analysis

OpenSym used the gratis version of EasyChair to manage the conference which doesn’t allow chairs to export data. As a result, data used in this this postmortem was scraped from EasyChair using two Python scripts. Numbers and graphs were created using a knitr file that combines R visualization and analysis code with markdown to create the HTML directly from the datasets. I’ve made all the code I used to produce this analysis available in this git repository. I hope someone else finds it useful. Because the data contains sensitive information on the review process, I’m not publishing the data.

Introduction to R workshop

I recently taught a two-session workshop introducing R to Kellogg MBA students. I had  a few goals for the workshops:

  1. Convince students of the benefits of using text-based programming for data exploration and analysis
  2. Introduce basic programming concepts (e.g., variables, functions)
  3. Give students a basic understanding of how to do some fundamental data analysis tasks in R: importing, cleaning, visualizing, and modeling

Those are really big goals for only four hours. I decided to use the tidyverse as much as possible and not even teach base R syntax like ‘[,]’, apply, etc. I used the first session to show and explain code using the nycflights13 dataset. For the the second session we did a few more examples but mostly worked on exercises using a dataset from Wikia that I created (with help from Mako and Aaron Halfaker‘s code and data).

Learning R does have its downsides

Retrospection

Overall, I think that the workshops went pretty well. I think that students definitely have a better understanding and a better set of tools than I did after I had used R for four hours!

That being said, there was plenty of room for improvement. I am scheduled to teach another set of workshops early next year and I’m planning to make a few changes:

  1. Make both of the workshops more hands-on and interactive. I think I’ll divide the topics covered: the first workshop will be on importing, cleaning, and grouping data and the second will be on visualizing and creating inferential models.
  2. Get more help – teaching non-programmers R requires some hand-holding and individual attention. To be successful, I think a workshop like this requires 1 “TA” for every 8-10 students.
  3. Find a more relevant dataset. Although I actually learned a few things about my dataset that will help with my papers that use it, I think it would be better to have a dataset that is as similar as possible to what students will be working with in their careers.
  4. Connect the visualization and regression more directly to a specific analysis problem rather than as syntax-learning exercises.

Reuse this workshop!

I found some pretty good resources already in existence for introducing students to R, but none of them quite fit the scope of what I was looking for.  All of the code that I used (as well as some slides for the beginning of class) are on github and GPL licensed. Please reuse my work and submit pull requests!

Peer production between real utopia and naive Coaseanism?

Over at Crooked Timber, Henry Farrell and others recently held a book seminar to discuss Cory Doctorow’s Walkaway. The symposium led to an extended discussion between Henry, Cory, Henry again, and Yochai Benkler about Benkler’s early work on commons-based peer production, spaces of resistance in the contemporary information economy, and the state of peer production a little over fifteen years since Benkler introduced the term. This (far too long) post summarizes some of their key points as a way of starting to collect my own thoughts on these questions.

I haven’t read Walkaway yet (downloaded my DRM-free digital copy, but the fiction slot in my brain is currently occupied by Philip Pullman’s totally engrossing La Belle Sauvage), but I can’t wait to get to it. Cory says the book started as an exercise in projecting how the sociotechnical transformations Benkler laid out in Coase’s Penguin might facilitate the spread of utopian energies at the periphery of radically unequal societies not so different from our own:

It’s been 15 years since Benkler made the connection between “commons-based peer-production” and Coase…

Down and Out in the Magic Kingdom projected Slashdot karma and Napster superdistribution across a whole society as a way of illuminating the strengths and weaknesses of both. Walkaway tries to do the same with commons-based peer-production: what would a skyscraper look like if it was a Wikipedia-style project? How about a space program?

As a Coasean tale, Walkaway is one the battleground between the technological, Promethean left—which has promised to lift peasants up to the material comfort of lords—and the de-growth green left, which promises to bring lords down to the level of the peasants in the name of saving the planet.

and later:

This is (in my view) a Utopian vision. It supposes that the Bohemian projects that even the most buttoned-down societies allow at their margins can breed real discontent and nurture and sustain it into something that genuinely challenges its host… They provided real-world lessons on which tactics worked and where the weaknesses were. They were battles, not the war. The only thing more extraordinary than a social justice prevailing at all is for it to prevail on its first outing, or second, or third.

In his contribution to the seminar, Henry points to Cory’s assumption that “exit” (in Hircshman’s sense) remains viable in a society pervaded by vast power inequalities, surveillance capabilities, and an (increasingly weaponized) disregard for privacy:

Again, Doctorow’s book isn’t an exercise in predictive science – he’s not saying that things will be so. But he is saying, I think, that things could and should be so, or sort-of so. Walkaway is quite unashamedly a didactic book in the way that earlier books such as Homeland were didactic – he has a very clear message to get across. In conversations with Steve Berlin Johnson years ago, I came up with the term BoingBoing Socialism to refer to a specific set of ideas associated with Doctorow and the people around him – that free exchange of ideas unimpeded by intellectual property law and the like, together with transformative technologies of manufacture, could open up a path towards a radically egalitarian future. Unless I’m seriously mistaken (in which case I’m sure that Doctorow will tell me), Walkaway wants to do two things – to argue for why such a future might be attractive, and to suggest that something like this future could be feasible.

For Henry, the implications boil down to questions of power and the role powerful entities play in shaping the lives of even the most peripheral, socially excluded groups within a society. He also (later on) expresses skepticism at the political prospects of the revolutionary vision of “BoingBoing Socialism” that adopts a rhetoric of contingency and self-marginalization as its platform for change.

Ronald Coase. 2003, U of Chicago Law School.

In a followup post, Henry elaborates a claim that Benkler engaged in a sort of naive Coasean disregard for power relations when he laid out the definitional statements on peer production. Henry says Benkler emphasized transaction cost and efficiency-centric explanations for the potential of peer production to substitute for firm or market-based modes of knowledge production and exchange:

Power relationships often explain who gets what, and which forms of organization are taken up, and which fall by the wayside. In general, forms of production that are (a) more efficient, but (b) inconvenient or unprofitable for powerful actors, are probably not going to be taken up, since those powerful actors will block them. Yet if one starts from an efficiency perspective, it is very hard to build power relations in, since one believes that change in practices and institutions is not driven by power relations but by efficiency.

and later:

What this means, if you take it seriously, is that Coaseian coordination is a special case of bargaining. Broadly speaking, Coaseian processes will lead to efficient outcomes only under very specific circumstances – when the actors have symmetrical breakdown values, as in the first game, so that neither of them is able to prevail over the other. More simply put, the Coase transaction cost account of how efficient institutions emerge will only work when all actors are more or less equally powerful. Under these conditions, it is perfectly alright to assume as Coase (and Benkler by extension) do, that efficiency considerations rather than power relations will drive change. In contrast, where there are significant differences of power, actors will converge on the institutions that reflect the preferences of powerful actors, even if those institutions are not the most efficient possible.

and finally:

In short – we need to distinguish between the rhetorical claims that technological change will bring openness along with it, and the (far more sustainable) claim that technology will probably only have openness enhancing benefits in a world where we are already dealing with the underlying power relations.

Benkler responds that Farrell is right to question his (Benkler’s) approach to power, but wrong in that the failure of his (Benkler’s) arguments in Coase’s Penguin and The Wealth of Networks is not driven by naive Coaseanism, but a different dimension of power entirely:

My primary mistake in my work fifteen years ago, and even ten, was not ignoring the role of power in shaping market patterns, but in understating the extent to which the new “market actors who will build the tools that make this population better able…” will themselves become the new incumbent market actors who will shape the environment to increase and lock-in their power. That is certainly a mistake in reading the landscape of power grabs, and I have tried to correct over the intervening years, most recently by offering a map of what has developed in the past decade…

In other words, today’s Benkler argues that yesterday’s Benkler underestimated the adaptive capacities of various incumbent powers as well as the way that a continuously shifting technical, regulatory, and political environment would alter the landscape along the way.

All of this speaks to an ongoing conversation Mako and I have been having about the past, present, and future of peer production. A pessimistic account might run like this: peer production thrived from ~1995-2008 in part because incumbent firms and private actors had not figured out how to capitalize on the possibilities for community-based provision of resources unlocked by the diffusion of digitally networked communications infrastructure. Now that increasing numbers of firms have done so, there is no going back. Large firms as well as their venture-funded spawn will continue to eat peer production communities’ lunch, undermining their viability as well as their autonomy. Peer production as we know it will eventually disappear, becoming a curious relic of a more naive era when the electronic frontier remained an unsettled, experimental space.

Another possibility, arguably more optimistic, can be seen in Benkler and Doctorow’s contributions to this exchange. Rather than consigning peer production to the dustbin of history, they both suggest that room for maneuver (or “degrees of freedom” in Benkler’s terms) will remain at the margins of the networked information economy and that communities of “walkaways” may persist in experimenting with “real utopian” autonomous alternatives to the more extractive, winner-take-all models of “supercapitalist” knowledge production and exchange. Doctorow’s fiction seems to explore the (hopeful) potential of these walkaway communities to generate radical, systematic transformation. Benkler, in his more recent writings, holds out some hope, but of a highly contingent, tenuous, and circumscribed sort.

The original posts are worth a read.

Ants!

cover of

I recently read Deborah M. Gordon’s Ant Encounters and thought I’d summarize some thoughts about it. Gordon is a Professor of Biology at Stanford. The book pulls together several decades of research (hers and others’) on the behavior and ecology of ants. In it, Gordon makes nuanced claims about the importance of communication and interaction for distributed collective behavior in clear, non-technical language. Many of the findings should inspire people (like me) interested in understanding the organization of collective behavior in humans.

Gordon argues that ant behavior and colony dynamics encompass a complex system driven by patterns of interactions, information exchange, and environmental influences. She contrasts this with more deterministic accounts of ants prevalent in earlier scientific literature and popular culture. Gordon emphasizes how ants operate by behavioral heuristics and information processing rather than a fixed set of rules or genetically encoded traits.

Picture of an argentine ant
Argentine ant (cc-by-sa, Penarc, Wikimedia Commons)

Consider the division of labor within an ant colony. The prevailing (wrong) view depicts ants born into a pre-specified, genetically determined “caste” which has a clearly-defined task within a hierarchically structured colony. Following this story, the Queen of the colony births out larva who grow into task-specialized sterile adults. Individuals within each caste supposedly possess physical traits that support their specialization as foragers, trash removers, larva-tenders, patrollers, or whatever. Each individual supposedly pursues their specialized task tirelessly until death.

It turns out that this account reflects a mixture of reasonable misinterpretation and fantastical thinking. First off, Gordon notes, ants change tasks within their life course. Today’s larva-tender may be tomorrow’s forager. These changes do not entail biological changes within each ant (although there seems to be evidence that ants do tend to adopt specific tasks at specific stages of their lives within a colony), but instead reflect responses to interactions with other members of the colony and external forces shaping those interactions. In a younger, less populous colony, ants may change tasks in response to immediate needs and threats that arise suddenly. In larger, more mature colonies where things are less likely to change suddenly, many ants may have more stable activities. Some ants in large colonies even literally sit around doing nothing because the information they receive from their nest-mates indicates that the colonies needs are being met. None of this is fixed by genetic encoding or hierarchical commands.

Second, Gordon shows how ants respond probabilistically to local stimuli. Individual ants, it turns out, act a lot like heuristic distributed sensors or nodes in a communications networkeach with some likelihood of changing its behavior depending on the feedback it receives from its environment. They are not automatons with deterministic programming to pursue a single-minded course of action.

Third, Gordon shows how colonies as a whole change in reaction to their environments and collective interactions. If one colony finds itself in proximity to another, the individuals within it may alter how much collective effort is dedicated to specific tasks depending on the species, size, and temperament of its neighbors. Individual ants respond to the number of nest-mates and neighbors they encounter. If their last ten encounters were with foragers from their home nest returning with food to feed the larval brood, they may continue to go about their business uninterrupted. As the portion of recent interactions includes outsiders or nest-mates responding frantically to an unwelcome intruder of some sort, the probability rises that the next ant will change its behavior in response (maybe to start running around in a panic or bite an intruder).

A picture of harvester ants
Harvester ants collecting seeds (cc-by-sa Donkey Shot, Wikimedia Commons)

Through many examples, Gordon conveys how patterns of collective ant behavior emerge and adapt to local circumstances without a centralized coordination mechanism or hierarchy of control. She describes this almost entirely without recourse to the jargon of complexity theory or complex systems research.

A concrete, measured, and example-driven account of how actually existing complex systems work is maybe the most impressive achievement of the book. Many texts discuss complexity in human and ecological systems, but none that I have read do so with the clarity of Ant Encounters. While I should read more books on these topics, more people in my little corner of the research world should read Gordon’s work too.

Ant Encounters ultimately left me excited to pursue some of the potential extensions and connections between Gordon’s work and research on human social systems and organizations. For example, I’d love to follow up on her comment that higher interaction frequency is associated with colony growth or survival (I currently forget which). Would such a finding hold up in the context of human organizations? If so, what would it look like and mean in the context of building effective peer production systems? Gordon has also written elsewhere about some of the potential connections between ant behavior, human organization, communication protocols. Recent findings from Gordon and her collaborators show how ants follow a set of behavior protocols very similar to those encoded in the TCP specification (apparently, she likes to refer to this idea as “the Anternet“). I’m eager to read more of the scientific publications from Gordon and her collaborators to understand these ideas more deeply and to see how well they travel when applied to a species I know a little bit more about.

OpenSym 2017 Program Published

A few hours ago, OpenSym 2017 kicked off in Galway. For those that don’t know, OpenSym is the International Symposium on Wikis and Open Collaboration (it was called WikiSym until 2014). Its the premier academic venue focused on research on wikis, open collboration, and peer production.

This year, Claudia Müller-Birn and I served as co-chairs of the academic program. Acting as program chair for an ACM conference like OpenSym is more like being a journal editor than a conference organizer. Claudia and I drafted and publicized a call for papers, recruited Associate Chairs and members of a program committee who would review papers and make decisions, coordinated reviews and final decisions, elicited author responses, sent tons of email to notify everybody about everything, and dealt with problems as they came up. It was a lot of work! With the schedule set, and the proceedings now online, our job is officially over!

OpenSym reviewed 43 papers this year and accepted 20 giving the conference a 46.5% acceptance rate. This is similar to both the number of submissions and the acceptance rates for previous years.

In addition to papers, we received 3 extended abstracts for posters for the academic program and accepted 1. There were an additional 7 promising papers that were not accepted but whose authors were invited to present posters and who will be doing so at the conference. The authors of posters will have extended abstracted about their posters published in the non-archival companion proceedings.

The list of papers being published and presented at OpenSym includes:

The following extended abstracts for posters will be published in the companion to the proceedings:

There was also a doctoral consortium and a non-academic ”industry track” which Claudia and I weren’t involved in coordinating.

As part of running the program, we tried a bunch of new things this year including:

  • A move away from separate tracks back to a singlec combined model with Associate Chairs.
  • Bidding for papers among both Associate Chairs and normal PC members.
  • An author rebuttal/response period where authors got to respond to reviews and reviewers.
  • An elimination of page limits for papers. This meant that the category of notes also disappeared. Reviewers were instructed to evaluate the degree to which papers’ contributions were commensurate to their length.

I’m working on a longer post that will evaluate these changes. Until then, enjoy Galway if you were lucky enough to be there. If you couldn’t make it, enjoy the proceedings online!

You can learn more about OpenSym on it’s Wikipedia article on the OpenSym website. You can find details on the schedule and the program itself at its temporary home on the OpenSym website. I’ll update this page with a link to the ACM Digital Library page when it gets posted.

Testing Our Theories About Surviving an “Eternal September”

Graph of subscribers and moderators over time in /r/NoSleep. The image is taken from our 2016 CHI paper.

Last year at CHI 2016, we published a qualitative study examining the effects of a large influx of newcomers to the /r/nosleep online community in Reddit. Our study began with the observation that most research on sustained waves of newcomers focuses on the destructive effect of newcomers and frequently invokes Usenet’s infamous “Eternal September.” Our qualitative study argued that the /r/nosleep community managed its surge of newcomers gracefully through strategic preparation by moderators, technological systems to reign in on norm violations, and a shared sense of protecting the community’s immersive environment among participants.

We are thrilled that, less a year after the publication of our study, Zhiyuan “Jerry” Lin and a group of researchers at Stanford have published a quantitative test of our study’s findings! Lin analyzed 45 million comments and upvote patterns from 10 Reddit communities that a massive inundation of newcomers like the one we studied on /r/nosleep. Lin’s group found that these communities retained their quality despite a slight dip in its initial growth period.

Our team discussed doing a quantitative study like Lin’s at some length and our paper ends with a lament that our findings merely reflected, “propositions for testing in future work.” Lin’s study provides exactly such a test! Lin et al.’s results suggest that our qualitative findings generalize and that sustained influx of newcomers need not doom a community to a descent into an “Eternal September.” Through strong moderation and the use of a voting system, the subreddits analyzed by Lin appear to retain their identities despite the surge of new users.

There are always limits to research projects work—quantitative and qualitative. We think the Lin’s paper compliments ours beautifully, we are excited that Lin built on our work, and we’re thrilled that our propositions seem to have held up!

This blog post was written with Benjamin Mako Hill. Our paper about /r/nosleep, written with Mako Hill and Andrés Monroy-Hernández, was published in the Proceedings of CHI 2016 and is released as open access. Lin’s paper was published in the Proceedings of ICWSM 2017 and is also available online.

Learning to code in one’s own language

Millions of young people from around the world are learning to code. Often, during their learning experiences, these youth are using visual block-based programming languages like Scratch, App Inventor, and Code.org Studio. In block-based programming languages, coders manipulate visual, snap-together blocks that represent code constructs instead of textual symbols and commands that are found in more traditional programming languages.

The textual symbols used in nearly all non-block-based programming languages are drawn from English—consider “if” statements and “for” loops for common examples. Keywords in block-based languages, on the other hand, are often translated into different human languages. For example, depending on the language preference of the user, an identical set of computing instructions in Scratch can be represented in many different human languages:

Although my research with Benjamin Mako Hill focuses on learning, both Mako and I worked on local language technologies before coming back to academia. As a result, we were both interested in how the increasing translation of programming languages might be making it easier for non-English speaking kids to learn to code.

After all, a large body of education research has shown that early-stage education is more effective when instruction is in the language that the learner speaks at home. Based on this research, we hypothesized that children learning to code with block-based programming languages translated to their mother-tongues will have better learning outcomes than children using the blocks in English.

We sought to test this hypothesis in Scratch, an informal learning community built around a block-based programming language. We were helped by the fact that Scratch is translated into many languages and has a large number of learners from around the world.

To measure learning, we built on some of our our own previous work and looked at learners’ cumulative block repertoires—similar to a code vocabulary. By observing a learner’s cumulative block repertoire over time, we can measure how quickly their code vocabulary is growing.

Using this data, we compared the rate of growth of cumulative block repertoire between learners from non-English speaking countries using Scratch in English to learners from the same countries using Scratch in their local language. To identify non-English speakers, we considered Scratch users who reported themselves as coming from five primarily non-English speaking countries: Portugal, Italy, Brazil, Germany, and Norway. We chose these five countries because they each have one very widely spoken language that is not English and because Scratch is almost fully translated into that language.

Even after controlling for a number of factors like social engagement on the Scratch website, user productivity, and time spent on projects, we found that learners from these countries who use Scratch in their local language have a higher rate of cumulative block repertoire growth than their counterparts using Scratch in English. This faster growth was despite having a lower initial block repertoire. The graph below visualizes our results for two “prototypical” learners who start with the same initial block repertoire: one learner who uses the English interface, and a second learner who uses their native language.

Our results are in line with what theories of education have to say about learning in one’s own language. Our findings also represent good news for designers of block-based programming languages who have spent considerable amounts of effort in making their programming languages translatable. It’s also good news for the volunteers who have spent many hours translating blocks and user interfaces.

Although we find support for our hypothesis, we should stress that our findings are both limited and incomplete. For example, because we focus on estimating the differences between Scratch learners, our comparisons are between kids who all managed to successfully use Scratch. Before Scratch was translated, kids with little working knowledge of English or the Latin script might not have been able to use Scratch at all. Because of translation, many of these children are now able to learn to code.


This blog-post and the work that it describes is a collaborative project with Benjamin Mako Hill. You can read our paper here. The paper was published in the ACM Learning @ Scale Conference. We also recently gave a talk about this work at the International Communication Association’s annual conference. We have received support and feedback from members of the Scratch team at MIT (especially Mitch Resnick and Natalie Rusk), as well as from Nathan TeBlunthuis at the University of Washington. Financial support came from the US National Science Foundation.

The Community Data Science Collective Dataverse

I’m pleased to announce the Community Data Science Collective Dataverse. Our dataverse is an archival repository for datasets created by the Community Data Science Collective. The dataverse won’t replace work that collective members have been doing for years to document and distribute data from our research. What we hope it will do is get our data — like our published manuscripts — into the hands of folks in the “forever” business.

Over the past few years, the Community Data Science Collective has published several papers where an important part of the contribution is a dataset. These include:

Recently, we’ve also begun producing replication datasets to go alongside our empirical papers. So far, this includes:

In the case of each of the first groups of papers where the dataset was a part of the contribution, we uploaded code and data to a website we’ve created. Of course, even if we do a wonderful job of keeping these websites maintained over time, eventually, our research group will cease to exist. When that happens, the data will eventually disappear as well.

The text of our papers will be maintained long after we’re gone in the journal or conference proceedings’ publisher’s archival storage and in our universities’ institutional archives. But what about the data? Since the data is a core part — perhaps the core part — of the contribution of these papers, the data should be archived permanently as well.

Toward that end, our group has created a dataverse. Our dataverse is a repository within the Harvard Dataverse where we have been uploading archival copies of datasets over the last six months. All five of the papers described above are uploaded already. The Scratch dataset, due to access control restrictions, isn’t listed on the main page but it’s online on the site. Moving forward, we’ll be populating this new datasets we create as well as replication datasets for our future empirical papers. We’re currently preparing several more.

The primary point of the CDSC Dataverse is not to provide you with way to get our data although you’re certainly welcome to use it that way and it might help make some of it more discoverable. The websites we’ve created (like for the ones for redirects and for page protection) will continue to exist and be maintained. The Dataverse is insurance for if, and when, those websites go down to ensure that our data will still be accessible.


This post was also published on Benjamin Mako Hill’s blog Copyrighteous.

Adventures in onboarding new users on Wikipedia

I recently finished a paper that presents a novel social computing system called the Wikipedia Adventure. The system was a gamified tutorial for new Wikipedia editors. Working with the tutorial creators, we conducted both a survey of its users and a randomized field experiment testing its effectiveness in encouraging subsequent contributions. We found that although users loved it, it did not affect subsequent participation rates.

Start screen for the Wikipedia Adventure.

A major concern that many online communities face is how to attract and retain new contributors. Despite it’s success, Wikipedia is no different. In fact, researchers have shown that after experiencing a massive initial surge in activity, the number of active editors on Wikipedia has been in slow decline since 2007.

The number of active, registered editors (≥5 edits per month) to Wikipedia over time. From Halfaker, Geiger, and Morgan 2012.

Research has attributed a large part of this decline to the hostile environment that newcomers experience when begin contributing. New editors often attempt to make contributions which are subsequently reverted by more experienced editors for not following Wikipedia’s increasingly long list of rules and guidelines for effective participation.

This problem has led many researchers and Wikipedians to wonder how to more effectively onboard newcomers to the community. How do you ensure that new editors Wikipedia quickly gain the knowledge they need in order to make contributions that are in line with community norms?

To this end, Jake Orlowitz and Jonathan Morgan from the Wikimedia Foundation worked with a team of Wikipedians to create a structured, interactive tutorial called The Wikipedia Adventure. The idea behind this system was that new editors would be invited to use it shortly after creating a new account on Wikipedia, and it would provide a step-by-step overview of the basics of editing.

The Wikipedia Adventure was designed to address issues that new editors frequently encountered while learning how to contribute to Wikipedia. It is structured into different ‘missions’ that guide users through various aspects of participation on Wikipedia, including how to communicate with other editors, how to cite sources, and how to ensure that edits present a neutral point of view. The sequence of the missions gives newbies an overview of what they need to know instead of having to figure everything out themselves. Additionally, the theme and tone of the tutorial sought to engage new users, rather than just redirecting them to the troves of policy pages.

Those who play the tutorial receive automated badges on their user page for every mission they complete. This signals to veteran editors that the user is acting in good-faith by attempting to learn the norms of Wikipedia.

An example of a badge that a user receives after demonstrating the skills to communicate with other users on Wikipedia.

Once the system was built, we were interested in knowing whether people enjoyed using it and found it helpful. So we conducted a survey asking editors who played the Wikipedia Adventure a number of questions about its design and educational effectiveness. Overall, we found that users had a very favorable opinion of the system and found it useful.

Survey responses about how users felt about TWA.

 

Survey responses about what users learned through TWA.

We were heartened by these results. We’d sought to build an orientation system that was engaging and educational, and our survey responses suggested that we succeeded on that front. This led us to ask the question – could an intervention like the Wikipedia Adventure help reverse the trend of a declining editor base on Wikipedia? In particular, would exposing new editors to the Wikipedia Adventure lead them to make more contributions to the community?

To find out, we conducted a field experiment on a population of new editors on Wikipedia. We identified 1,967 newly created accounts that passed a basic test of making good-faith edits. We then randomly invited 1,751 of these users via their talk page to play the Wikipedia Adventure. The rest were sent no invitation. Out of those who were invited, 386 completed at least some portion of the tutorial.

We were interested in knowing whether those we invited to play the tutorial (our treatment group) and those we didn’t (our control group) contributed differently in the first six months after they created accounts on Wikipedia. Specifically, we wanted to know whether there was a difference in the total number of edits they made to Wikipedia, the number of edits they made to talk pages, and the average quality of their edits as measured by content persistence.

We conducted two kinds of analyses on our dataset. First, we estimated the effect of inviting users to play the Wikipedia Adventure on our three outcomes of interest. Second, we estimated the effect of playing the Wikipedia Adventure, conditional on having been invited to do so, on those same outcomes.

To our surprise, we found that in both cases there were no significant effects on any of the outcomes of interest. Being invited to play the Wikipedia Adventure therefore had no effect on new users’ volume of participation either on Wikipedia in general, or on talk pages specifically, nor did it have any effect on the average quality of edits made by the users in our study. Despite the very positive feedback that the system received in the survey evaluation stage, it did not produce a significant change in newcomer contribution behavior. We concluded that the system by itself could not reverse the trend of newcomer attrition on Wikipedia.

Why would a system that was received so positively ultimately produce no aggregate effect on newcomer participation? We’ve identified a few possible reasons. One is that perhaps a tutorial by itself would not be sufficient to counter hostile behavior that newcomers might experience from experienced editors. Indeed, the friendly, welcoming tone of the Wikipedia Adventure might contrast with strongly worded messages that new editors receive from veteran editors or bots. Another explanation might be that users enjoyed playing the Wikipedia Adventure, but did not enjoy editing Wikipedia. After all, the two activities draw on different kinds of motivations. Finally, the system required new users to choose to play the tutorial. Maybe people who chose to play would have gone on to edit in similar ways without the tutorial.

Ultimately, this work shows us the importance of testing systems outside of lab studies. The Wikipedia Adventure was built by community members to address known gaps in the onboarding process, and our survey showed that users responded well to its design.

While it would have been easy to declare victory at that stage, the field deployment study painted a different picture. Systems like the Wikipedia Adventure may inform the design of future orientation systems. That said, more profound changes to the interface or modes of interaction between editors might also be needed to increase contributions from newcomers.

This blog post, and the open access paper that it describes, is a collaborative project with Jake OrlowitzJonathan Morgan, Aaron Shaw, and Benjamin Mako Hill. Financial support came from the US National Science Foundation (grants IIS-1617129 and IIS-1617468), Northwestern University, and the University of Washington. We also published all the data and code necessary to reproduce our analysis in a repository in the Harvard Dataverse.