Category: Publications

Online communities have become ubiquitous, providing not only entertainment but wielding increasing cultural and political influence. While news organizations and researchers have focused a lot of attention on online communities after they become influential, very little is known about how or why they get started. Our survey of hundreds of Wikia.com founders shows that typical online communities are actually very different from the communities that are “in the news”. Online community founders have diverse motivations, but typically have modest goals which are focused on filling their own needs, and they don’t necessarily care if their projects ever get very big. Our research suggests that rather than being failures, small online communities are both intentional and common.

Most online communities are small —Our research is inspired by the skewed distribution of attention online. For example, these three graphs show the number of contributors to each subreddit, github project, and Wikipedia page. (Note the log scale – the reality is even more skewed than these plots make it appear).




In every case, there is a “long tail” of projects with very few contributions or attention, while the most popular projects get the lion’s share. It is perhaps unsurprising, then, that they also garner the majority of scholarly attention. However, what these graphs also show is that most online communities are very small.

Even when scholars include smaller communities in their analysis, they typically treat longevity and size as measures of success. Using this metric, the vast majority of new projects fail. So why do people start new online communities? Are they simply naive, not realizing that large-scale success is so rare? Are community founders trying to win the attention lottery?

Our Survey —We worked with some great folks at Wikia to send a survey to community founders right after they started their community. We received partial or full responses from hundreds of founders.

In addition to demographic information, we asked a set of thirteen questions about the motivations of founders, based on the contributor motivation literature, and seven questions about their goals for their community. We also asked founders about their plans for their community, and whether they were planning to follow some of the best practices for building and running online communities.

Founders have diverse motivations and modest goals — We found that Wikia founders have diverse motivations. We used PCA to identify four main motivations for creating new wikis: spreading information and building a community, problems with existing wikis, for fun or learning, and creating and publicizing personal content. Spreading information and building a community was the most common motivation, but each of these was marked as a primary motivation by multiple respondents.

We also found that the barriers to starting a new community – both technological and cognitive – are very low. Only 32% of founders reported planning on starting their wiki for a few weeks or longer, while fully 46% of founders had only planned it for a few hours or a few minutes.

As with motivations, founders had diverse goals. The most common top goal was the creation of high-quality information, with nearly half of respondents selecting it. Community longevity/activity and growth were also common goals.

Finally, we looked at whether there was a relationship between motivations and goals, and between goals and plans for community building. We found that those whose top goal was information quality were less likely to be motivated by fun and learning, and that they were less likely to plan on recruiting contributors or encouraging contributions. In future research, we are looking at how a founder’s goals and plans relate to membership and contribution growth.

So what? —We believe that platform designers and researchers should focus more of their resources on understanding small and short-lived communities. Our research suggests that the attention paid to the more popular and long-lived online communities has perpetuated a false assumption that all communities seek to become large and powerful. Indeed, our respondents are typically not seeking or even hoping for large-scale “success”.

In addition, we believe that in many contexts, understanding online communities can be augmented by focusing on founders. Platform designers can study founders to understand how users would like to use a system and researchers can do more to understand the differences between founders and other contributors.

There is also a need to generalize this research – founders on other online platforms (Reddit, github, etc.) may have a different set of motivations and goals (although we suspect that they will be similarly modest in their ambitions). Overall, there is lots of room for additional research on how and why things get started online.

The paper and data — If you liked this blog post, then you’ll love the full paper: Starting online communities: Motivations and goals of wiki founders. Even better, if you are planning to be at CHI 2017, come watch the talk!

This post (and the paper) were written by Jeremy Foote, Aaron Shaw and Darren Gergle. The charts at the beginning of the post were created using data from the great public datasets at Big Query. Anonymized results of the survey are publicly available, and code is coming.

With more than 10 million users, the Scratch online community is the largest online community where kids learn to program. Since it was created, a central goal of the community has been to promote “remixing” — the reworking and recombination of existing creative artifacts. As the video above shows, remixing programming projects in the current web-based version of Scratch is as easy is as clicking on the “see inside” button in a project web-page, and then clicking on the “remix” button in the web-based code editor. Today, close to 30% of projects on Scratch are remixes.

Remixing plays such a central role in Scratch because its designers believed that remixing can play an important role in learning. After all, Scratch was designed first and foremost as a learning community with its roots in the Constructionist framework developed at MIT by Seymour Papert and his colleagues. The design of the Scratch online community was inspired by Papert’s vision of a learning community similar to Brazilian Samba schools (Henry Jenkins writes about his experience of Samba schools in the context of Papert’s vision here), and a comment Marvin Minsky made in 1984:

Adults worry a lot these days. Especially, they worry about how to make other people learn more about computers. They want to make us all “computer-literate.” Literacy means both reading and writing, but most books and courses about computers only tell you about writing programs. Worse, they only tell about commands and instructions and programming-language grammar rules. They hardly ever give examples. But real languages are more than words and grammar rules. There’s also literature – what people use the language for. No one ever learns a language from being told its grammar rules. We always start with stories about things that interest us.

In a new paper — titled “Remixing as a pathway to Computational Thinking” — that was recently published at the ACM Conference on Computer Supported Collaborative Work and Social Computing (CSCW) conference, we used a series of quantitative measures of online behavior to try to uncover evidence that might support the theory that remixing in Scratch is positively associated with learning.

Of course, because Scratch is an informal environment with no set path for users, no lesson plan, and no quizzes, measuring learning is an open problem. In our study, we built on two different approaches to measure learning in Scratch. The first approach considers the number of distinct types of programming blocks available in Scratch that a user has used over her lifetime in Scratch (there are 120 in total) — something that can be thought of as a block repertoire or vocabulary. This measure has been used to model informal learning in Scratch in an earlier study. Using this approach, we hypothesized that users who remix more will have a faster rate of growth for their code vocabulary.

Controlling for a number of factors (e.g. age of user, the general level of activity) we found evidence of a small, but positive relationship between the number of remixes a user has shared and her block vocabulary as measured by the unique blocks she used in her non-remix projects. Intriguingly, we also found a strong association between the number of downloads by a user and her vocabulary growth. One interpretation is that this learning might also be associated with less active forms of appropriation, like the process of reading source code described by Minksy.

The second approach we used considered specific concepts in programming, such as loops, or event-handling. To measure this, we utilized a mapping of Scratch blocks to key programming concepts found in this paper by Karen Brennan and Mitchel Resnick. For example, in the image below are all the Scratch blocks mapped to the concept of “loop”.

We looked at six concepts in total (conditionals, data, events, loops, operators, and parallelism). In each case, we hypothesized that if someone has had never used a given concept before, they would be more likely to use that concept after encountering it while remixing an existing project.

Using this second approach, we found that users who had never used a concept were more likely to do so if they had been exposed to the concept through remixing. Although some concepts were more widely used than others, we found a positive relationship between concept use and exposure through remixing for each of the six concepts. We found that this relationship was true even if we ignored obvious examples of cutting and pasting of blocks of code. In all of these models, we found what we believe is evidence of learning through remixing.

Of course, there are many limitations in this work. What we found are all positive correlations — we do not know if these relationships are causal. Moreover, our measures do not really tell us whether someone has “understood” the usage of a given block or programming concept.However, even with these limitations, we are excited by the results of our work, and we plan to build on what we have. Our next steps include developing and utilizing better measures of learning, as well as looking at other methods of appropriation like viewing the source code of a project.

This blog post and the paper it describes are collaborative work with Sayamindu Dasgupta, Andrés Monroy-Hernández, and William Hale. The paper is released as open access so anyone can read the entire paper here. This blog post was also posted on Benjamin Mako Hill’s blog, on Sayamindu Dasgupta’s blog and on Medium by the MIT Media Lab.

This post was originally published on Benjamin Mako Hill‘s blog Copyrighteous.

In wikis, redirects are special pages that silently take readers from the page they are visiting to another page. Although their presence is noted in tiny gray text (see the image below) most people use them all the time and never know they exist. Redirects exist to make linking between pages easier, they populate Wikipedia’s search autocomplete list, and are generally helpful in organizing information. In the English Wikipedia, redirects make up more than half of all article pages.

Over the years, I’ve spent some time contributing to to Redirects for Discussion (RfD). I think of RfD as like an ultra-low stakes version of Articles for Deletion where Wikipedians decide whether to delete or keep articles. If a redirect is deleted, viewers are taken to a search results page and almost nobody notices. That said, because redirects are almost never viewed directly, almost nobody notices if a redirect is kept either!

I’ve told people that if they want to understand the soul of a Wikipedian, they should spend time participating in RfD. When you understand why arguing about and working hard to come to consensus solutions for how Wikipedia should handle individual redirects is an enjoyable way to spend your spare time — where any outcome is invisible — you understand what it means to be a Wikipedian.

That said, wiki researchers rarely take redirects into account. For years, I’ve suspected that accounting for redirects was important for Wikipedia research and that several classes of findings were noisy or misleading because most people haven’t done so. As a result, I worked with my colleague Aaron Shaw at Northwestern earlier this year to build a longitudinal dataset of redirects that can capture the dynamic nature of redirects. Our work was published as a short paper at OpenSym several months ago.

It turns out, taking redirects into account correctly (especially if you are looking at activity over time) is tricky because redirects are stored as normal pages by MediaWiki except that they happen to start with special redirect text. Like other pages, redirects can be updated and changed over time are frequently are. As a result, taking redirects into account for any study that looks at activity over time requires looking at the text of every revision of every page.

Using our dataset, Aaron and I showed that the distribution of edits across pages in English Wikipedia (a relationships that is used in many research projects) looks pretty close to log normal when we remove redirects and very different when you don’t. After all, half of articles are really just redirects and, and because they are just redirects, these “articles” are almost never edited.

Another puzzling finding that’s been reported in a few places — and that I repeated myself several times — is that edits and views are surprisingly uncorrelated. I’ll write more about this later but the short version is that we found that a big chunk of this can, in fact, be explained by considering redirects.

We’ve published our code and data and the article itself is online because we paid the ACM’s open access fee to ransom the article.

For more details see the paper: Hill, Benjamin Mako, and Aaron Shaw. (2014) “Consider the Redirect: A Missing Dimension of Wikipedia Research.” In Proceedings of the 10th International Symposium on Open Collaboration (OpenSym 2014). ACM Press, 2014.