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!
This graph shows the number of people contributing to Wikipedia over time:
The figure comes from “The Rise and Decline of an Open Collaboration System,” a well-known 2013 paper that argued that Wikipedia’s transition from rapid growth to slow decline in 2007 was driven by an increase in quality control systems. Although many people have treated the paper’s finding as representative of broader patterns in online communities, Wikipedia is a very unusual community in many respects. Do other online communities follow Wikipedia’s pattern of rise and decline? Does increased use of quality control systems coincide with community decline elsewhere?
The original “Rise and Decline” paper (I’ll abbreviate it “RAD”) was written by Aaron Halfaker, R. Stuart Geiger, Jonathan T. Morgan, and John Riedl. They analyzed data from English Wikipedia and found that Wikipedia’s transition from rise to decline was accompanied by increasing rates of newcomer rejection as well as the growth of bots and algorithmic quality control tools. They also showed that newcomers whose contributions were rejected were less likely to continue editing and that community policies and norms became more difficult to change over time, especially for newer editors.
Our paper, just published in the CHI 2018 proceedings, replicates most of RAD’s analysis on a dataset of 769 of the largest wikis from Wikia that were active between 2002 to 2010. We find that RAD’s findings generalize to this large and diverse sample of communities.
I can walk you through some of the key findings. First, the growth trajectory of the average wiki in our sample is similar to that of English Wikipedia. As shown in the figure below, an initial period of growth stabilizes and leads to decline several years later.
We also found that newcomers on Wikia wikis were reverted more and continued editing less. As on Wikipedia, the two processes were related. Similar to RAD, we also found that newer editors were more likely to have their contributions to the “project namespace” (where policy pages are located) undone as wikis got older. Indeed, the specific estimates from our statistical models are very similar to RAD’s for most of these findings!
There were some parts of the RAD analysis that we couldn’t reproduce in our context. For example, there are not enough bots or algorithmic editing tools in Wikia to support statistical claims about their effects on newcomers.
At the same time, we were able to do some things that the RAD authors could not. Most importantly, our findings discount some Wikipedia-specific explanations for a rise and decline. For example, English Wikipedia’s decline coincided with the rise of Facebook, smartphones, and other social media platforms. In theory, any of these factors could have caused the decline. Because the wikis in our sample experienced rises and declines at similar points in their life-cycle but at different points in time, the rise and decline findings we report seem unlikely to be caused by underlying temporal trends.
The big communities we study seem to have consistent “life cycles” where stabilization and/or decay follows an initial period of growth. The fact that the same kinds of patterns happen on English Wikipedia and other online groups implies a more general set of social dynamics at work that we do not think existing research (including ours) explains in a satisfying way. What drives the rise and decline of communities more generally? Our findings make it clear that this is a big, important question that deserves more attention.
We hope you’ll read the paper and get in touch by commenting on this post or emailing me if you’d like to learn or talk more. The paper is available online and has been published under an open access license. If you really want to get into the weeds of the analysis, we will soon publish all the data and code necessary to reproduce our work in a repository on the Harvard Dataverse.
I will be presenting the project this week at CHI in Montréal on Thursday April 26 at 9am in room 517D. For those of you not familiar with CHI, it is the top venue for Human-Computer Interaction. All CHI submissions go through double-blind peer review and the papers that make it into the proceedings are considered published (same as journal articles in most other scientific fields). Please feel free to cite our paper and send it around to your friends!
This blog post, and the open access paper that it describes, is a collaborative project with Aaron Shaw, and Benjamin Mako Hill. Financial support came from the US National Science Foundation (grants IIS-1617129, IIS-1617468, and GRFP-2016220885 ), Northwestern University, the Center for Advanced Study in the Behavioral Sciences at Stanford University, and the University of Washington. This project was completed using the Hyak high performance computing cluster at the University of Washington.
You may have heard of Change.org. It’s a popular online petitioning platform. You may have even noticed there can many online petitions about popular topics. For instance, it is easy to find dozens of petitions protesting the Lychee and Dog Meat Festival with varying levels of support.
Imagine you want to start an online petition. You might worry if your petition is very similar to other people’s petitions that already have signatures. These other petitions have a head start and will get all the attention. That said, if nobody has made any similar petitions, maybe that’s because the issue you are petitioning about doesn’t yet have a lot of popular support. You might also worry if your petition is unusual. Which of these two worries (making a duplicate petition and making a petition no one cares about) should concern you, dear petition creator? In my research, I set out to answer this question. The project is still in progress. I recently presented it as a poster at CSCW ’17.
Sociologists of organizational ecology considered similar questions about businesses and social movement organizations. They wanted to explain why organizations were more likely to die when an industry was young or old, but less likely to die in between. They argued that density, or the number of organizations in the population, was tied both to processes of legitimation and competition. There aren’t many firms in unproven industries because it’s not clear the industry will succeed, but when an industry is mature it becomes competitive. Everybody wants a piece of the pie, but you might not get enough pie to survive! This notion is called density dependence theory.
I think it is intuitive to apply this logic to online petitions and topics. If you make a petition about a low-density topic, chances for success should be lower because the petition is more likely to be unusual or illegitimate. However if you make a petition in a high-density topic, now you have to worry about competition with all the other petitions in the topic. You want your petition to be original, but not weird!
To collect data to test this theory, I downloaded a large set of petitions from Change.org, spam filtered them, and removed very short ones. Next I used LDA topic modeling to group petitions into topics. This makes it possible to assign petitions to points in a topic space. The more crowded this part of topic space, the denser the petition’s environment.
Finally, I used a regression model to predict petition signature counts. Since density dependence theory predicts that the relationship between density and signature count is shaped like an upside-down U, I included a quadratic term for density. The plot below shows that observed relationship between density in topic space and signature count is what the theory predicted. The darkness of the lines at the bottom of the plot show that most petitions are in less dense parts of topic space. So you, dear petition creator, should worry about competition and legitimacy, but worry about legitimacy first!
I’m excited by this result because it shows interesting similarities between efforts to organize coordinated activism online and traditional organizations like firms. I’m planning to apply this method to other forms of online coordination like wikis and online communities.
This blog-post and the work it describes is a collaborative project between Nate TeBlunthuis, Benjamin Mako Hill and Aaron Shaw. We are still at work writing this project up as a research article. The work has been supported by the US National Science Foundation