Connected Action » roles

iConference 2011 Wiki roles paper awarded best paper – University of Washington, Seattle, WA

Marc Smith — Sun, 24 Apr 2011 16:15:22 +0000

http://www.ischools.org/iConference11/2011index/

Our paper, Finding Social Roles in Wikipedia, about the variety of roles people perform in Wikis received the best paper award (along with 4 others) in a field of 86 papers. The 2011 iConference accepted 86 papers, and had about 550 attendees.

The paper is authored by: Howard T. Welser at Ohio University, Austin Lin at Cornell University and Microsoft, Dan Cosley, Fedor Dokshin, Gueorgi Kossinets and Geri Gay at Cornell University, and Marc Smith from Connected Action.

The paper pdf pre-print is available here: http://oak.cats.ohiou.edu/~welser/Welser.Cosley.plus.Wiki.Roles.pdf

The link to the ACM abstract and pdf: http://portal.acm.org/citation.cfm?id=1940778&CFID=9933318&CFTOKEN=58981138

Abstract: This paper investigates some of the social roles people play in the online community of Wikipedia. We start from qualitative comments posted on community oriented pages, wiki project memberships, and user talk pages in order to identify a sample of editors who represent four key roles: substantive experts, technical editors, vandal fighters, and social networkers. Patterns in edit histories and egocentric network visualizations suggest potential “structural signatures” that could be used as quantitative indicators of role adoption. Using simple metrics based on edit histories we compare two samples of Wikipedians: a collection of long term dedicated editors, and a cohort of editors from a one month window of new arrivals. According to these metrics, we find that the proportions of editor types in the new cohort are similar those observed in the sample of dedicated contributors. The number of new editors playing helpful roles in a single month’s cohort nearly equal the number found in the dedicated sample. This suggests that informal socialization has the potential provide sufficient role related labor despite growth and change in Wikipedia. These results are preliminary, and we describe several ways that the method can be improved, including the expansion and refinement of role signatures and identification of other important social roles.

2009 ICWSM Poster – Distinguishing Knowledge vs Social Capital in Social Media with Roles and Context

Vlad43210 — Sun, 17 May 2009 18:22:01 +0000

On Tuesday night Marc Smith and I will be presenting the poster for our paper, “Distinguishing Knowledge vs Social Capital in Social Media with Roles and Context” at the International Conference for Weblogs and Social Media. You can find the poster, co-authored with Marc Smith (Telligent Systems), Lise Getoor (University of Maryland) and Howard T. Welser (Ohio University), here. The full text of the paper has more information, but the poster is a good summary of the key concepts in the paper:

What roles do people play in social media?
What contexts shape user behavior in social media?
How can we leverage roles and context together to predict future user behavior (in terms of contribution type) from past user behavior?

The specific research question being addressed is: can we predict whether a particular contribution to Live Q&A (a Microsoft-sponsored community question answering site) will be contain factual information, or discussion / chat. It is possible to do the prediction based on the text of the contribution, but such an approach focuses entirely on the content, and not on the actor – the user who is making the contribution. If we leverage actor-centric information (what role does he/she play in the community: an “answer person” or a “discussion person”? is he making the contribution in a discussion-oriented context, such as implied by tagging the contribution as “fun,” or a fact-oriented context, such as implied by tagging the contribution as “math”?), we find we can build a decent predictor at very low cost with very few variables. If we use just role information or just context information, we do reasonably well… but if we use both, we do *much* better. While the question we’re answering here is quite specific, the advantage of our approach is that it can be applied to almost any social media context – any place online where users can both contribute content and interact with others. We could just as easily create a predictor for contributions to Yahoo! Answers, or even to Wikipedia (if we had the relevant data). This is definitely food for thought / opportunity for future work

Social Media stakeholders: community roles

Marc Smith — Sun, 21 Dec 2008 21:12:41 +0000

Here are a few of the major stakeholders that gather around any social media effort. Each role can be further divided into sub roles who specialize in particular kinds of behavior like starting discussions, arguments, or answering questions. Each stakeholder has information needs that are related but somewhat different from other stakeholders. Building effective social media systems requires delivering the right information to each population.

Hosts pay for the servers and want R.O.I. They need to track impact of various kinds of investment in the community and decide where and how allocate resources

Managers - May have a presence in the community and want to demonstrate R.O.I. and get positive responses from customers. They need to target moderation, software and hardware resources.

Leaders provide answers, social support, institutional memory, and police the space. Want visibility and recognition for their efforts. They spend a significant amount of their personal and/or work time contributing content for the community. They write FAQ, how-to articles, shares code, etc.

Participants show up in the community, discussing topics, generating questions, providing answers, contributing with content and code (lightly!) Often looking for help, they have various levels of expertise, expect quick and accurate answers. They want guidance to high quality community content.

Silent searchers do not contribute or ask questions, they just consume the product of the interaction of the leaders and questioners, searching for previous answers to similar problems.

Researchers track a series of indicators and make sure that the reason for people to join, participate and revisit are strong. They explore patterns and structures in the community datasets and experiment with new interfaces and analysis.