Forming, storming, norming, performing, and …chloroforming?

In 1965, Bruce Tuckman proposed a “developmental sequence in small groups.” According to his influential theory, most successful groups go through four stages with rhyming names:

  1. Forming: Group members get to know each other and define their task.
  2. Storming: Through argument and disagreement, power dynamics emerge and are negotiated.
  3. Norming: After conflict, groups seek to avoid conflict and focus on cooperation and setting norms for acceptable behavior.
  4. Performing: There is both cooperation and productive dissent as the team performs the task at a high level.

Fortunately for organizational science, 1965 was hardly the last stage of development for Tuckman’s theory!

Twelve years later, Tuckman suggested that adjourning or mourning reflected potential fifth stages (Tuckman and Jensen 1977). Since then, other organizational researchers have suggested other stages including transforming and reforming (White 2009), re-norming (Biggs), and outperforming (Rickards and Moger 2002).

What does the future hold for this line of research?

To help answer this question, we wrote a regular expression to identify candidate words and placed the full list is at this page in the Community Data Science Collective wiki.

The good news is that despite the active stream of research producing new stages that end or rhyme with -orming, there are tons of great words left!

For example, stages in a group’s development might include:

  • Scorning: In this stage, group members begin mocking each other!
  • Misinforming: Groups that reach this stage start producing fake news.
  • Shoehorning: These groups try to make their products fit into ridiculous constraints.
  • Chloroforming: Groups become languid and fatigued?

One benefit of keeping our list in the wiki is that the organizational research community can use it to coordinate! If you are planning to use one of these terms—or if you know of a paper that has—feel free to edit the page in our wiki to “claim” it!


Although credit for this post goes primarily to Jeremy Foote and Benjamin Mako Hill, the other Community Data Science Collective members can’t really be called blameless in the matter either.

Summer Institute in Computational Social Science

For the second year, Matt Salganik and Chris Bail are running a two-week Summer Institute in Computational Social Science at Duke Univeristy. The goal of the institute is to bring social scientists and data scientists together to learn about computational social science, which can be described as a merger of their two fields.

This year, there are seven partner locations where local students livestream the activities from Duke and learn from local computational social scientists.  Both of our universities are among the partner locations.

At the University of Washington, Kaylea and Charlie have both been accepted as participants in the UW summer institute. At Northwestern University, Jeremy is helping to organize SICSS Chicago.

Much of the work that we do in the Community Data Science Collective could be considered computational social science, and we are excited about the potential for  computational methods in social science. This is a great program for helping to disseminate computational social science approaches and train the next generation of computational social scientists. The Community Data Science Collective is happy to be a sponsor of the Chicago partner location.

Photo of the SICSS participants in Chicago, sponsored by CDSC!

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!

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!

Why do people start new online communities and projects?

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).

Reddit graph


Github graph

Wikipedia graphIn 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.

Wikia homepage
Wikia homepage as it appeared during our data collection (via archive.org) with the invitation to found a new wiki highlighted. Twilight was really big in 2010.

 

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.

Motivations by goals
Plans by goals
Distribution of founder motivations and plans, based on whether their top goal is community or information quality.

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.