The Facebook Project: Fostering Multidisciplinary e-Social Science

Jeff Ginger | LIS590: Distributed Knowledge | Bruce & Haythornthwaite | Revision 1 | 10.03.2008

A term or concept you learned (or learned to think about it in a new way), relevant to the case; define and discuss this term

My Chosen Term: e-Social Science

The choice of such a blatant if not banal disciplinary subset might seem at first to be a cumbersome oversight, but the more I thought about the notion the more it seemed to make sense and foster connection of ideas.  We’ve spent a great deal of time discussing domains of knowledge and scientific practices but all too often this discourse has found itself housed within the realm of computing, engineering and natural science.  In an effort to discover ways e-science was relevant to my work as a social scientist and community organizer I began to scour related resources and came upon the movement.  Around the turn of the century a new iteration of e-science, that of the distinctly social-oriented flavor, began to circulate amongst e-science communities.  As the “hard science” realm depends increasingly on such techniques and finds more successful applications it becomes increasingly crucial for other areas to stake a claim in their development and use.  E-social science can be generally defined as the application of e-science methods, such as grid/cluster computing solutions and collaboratories, to the collection, analysis, sharing and circulation of social science data.

Often interdisciplinary collaboration takes place between (at least loosely) related fields but does not span greater gaps in the conceptualization and framing of problems as well as their correspondent proposed solutions (Zhu and Li 2007).  It’s a fairly common site to see an interdisciplinary effort between engineers, computer scientists, mathematicians or natural scientists. Likewise group collaborations between social and humanities researchers are also commonplace.  Each group might even bring in a token representative from the other side—the anthropologist to do a case study on a technology or the technical consultant to help deploy communications for a new educational model—but too rarely are studies built from the ground up based on mutual deep input.  If crossing knowledge divides necessitates the articulation of “often invisible, taken-for-granted knowledge-based asset specificities that is constrain what is recognized and accepted as practice in different fields or occupations involved in the collaboration” (Haythornthwaite 2006) then synthesis of significant cognitive diversity, such as the combining of humanities, social science, and natural science on equal footing in dialogue (or debate), is absolutely vital.  Cognitive diversity in the form of autonomous, independent and decentralized but well-connected persons could very well likely be a positive factor in aiding groups in coming to better decisions (Surowiecki 2005) and variance in approach among participants could help to better determine causes and impacts within a large policy question (Dale 2008).

Discovery of this term (or rather, type of e-science) didn’t drastically alter my way of thinking about the world but it did inspire me to think about the types of social science projects that might benefit from a multi-disciplinary collaboratory or supercomputing endeavor.  It also reminded me of a pertinent study.

A brief review of an article relevant to the case, which was not on the syllabus

Traditionally social science has been the primary determinant in policy formation for Universities in the US, especially when it comes to questions of race, ethnicity and affirmative action.  Many studies pertaining to these issues are comprised of concentrated qualitative measures while others try to establish a wide-scope understanding of social structures and relational processes.  Nearly all of these studies make use of datasets collected specifically for such policy questions.

A recent article by Adalbert Mayer and Steven L. Puller (2008), however, takes a stride in a different direction to analyze a series of distributed knowledge networks that were not built with the explicit purpose of research: Facebook.

Their paper, The Old Boy (and Girl) Network: Social Network Formation on University Campuses takes advantage of the vast dataset possibilities offered by the Facebook network before it became closed access.  Using administrative data they were able to document social (Facebook friend) connections between individuals at ten different Texas universities and determine the strongest predictors of social group formation and segmentation.  After controlling for a variety of measures, including socioeconomic background, ability and college activities they still found that race (not ethnicity in this case, it was purely visual) was a strongly related to social ties.  They then developed a model of social linking influences based on exogenous school environment and preferences for certain characteristics in one’s friends.  They had the benefit of being able to layer two datasets: formal university information on student demographics and social networking/clustering as well as personal data from Facebook.  Using their model they were able to simulate potential changes in policy that might address social segmentation based on race, such as the introduction of affirmative action systems that might bring in more racial minorities.  Their findings were interesting and generally indicated that such policy changes only have a limited potential to reduce racial segmentation within student formed social networks.  The implication is thus that policies or solutions ought to be based more around altering individual preferences and interaction related to race in addition to simply increasing diversity.

Now for most people familiar with race (racism) and social science this conclusion is nothing astoundingly new but what’s neat about this study is the way they’ve collected the data on such a large scale with a more organically developed (grown?) dataset.  What’s more is that the effort was conducted involving multiple locations and made use of computing technologies (modeling and analysis).

Your analysis of the case of knowledge and e-science drawing from the syllabus readings and your own additions

This paper connects to e-(social) science and distributed knowledge in several significant ways.
The way people make sense of their identities and consequently express them is fundamentally an ecology of tacit knowledge.  Facebook is a system teeming with discrete exchanges of information and community defined understandings; the way this all becomes embedded in interface and CMC (purposed or not, implicit or explicit) is dynamic, emergent and otherwise often difficult to measure.  All of these factors play a major role in the formation of Facebook friends networks—we don’t have to define from on high what it means to be Facebook friends—and that’s the beauty of the system, it’s a relatively accurate simulation of social and informational processes that we don’t even have to fully understand to measure.  The variables in the system are active producers of information (in other words it’s more human driven than it’s machine driven though the effect is distinctly a socio-technical circle) and since it’s more a reflection (or extension) of offline social networks than it is a composition or enabler of virtual ties (Lampe et al. 2006) it claims additional validity.  If the different elements (objects) in the Facebook system, call them profiles or coffee cups (Bruce 2008), are embedded forms of identity situated in an organic whole then they go beyond merely being symbols but active communicative bodies in conversation (boyd and Heer 2006).  These are not conversations or narrative stories in the sense we traditionally know them but might still be captured and visualized through analysis of the unified social networks (uptake is often around 90% of student populations!) they knit.  Mayer and Puller found one especially effective way to do this: they picked a tremendously complex question with many intersectional and undistinguished relations and answered it with an equally matched dataset.

Mayer and Puller were at the cusp of what could have been large-scale e-social science.  The dataset that is Facebook is one that is moderately diverse (and increasingly more so) and receives input from virtually everywhere in the US (and more than any other single SNS globally).  These two authors networked information from only ten universities to construct their experiment but it’s possible with public releases of Facebook data (like the recent Berkman Center set) that in the future such records could be centralized in an accessible location via online in collaboratories.  A collaboratory of this nature should probably go beyond just Facebook to include multiple social networks motivated by heterogeneous active agents, like YouTube or MySpace.  Researchers from many disciplines could partake in asking questions of the data as well as each of other.  The sheer complexity of relationships within these systems begs the use of grid and cluster computing.  Just think a researcher with the NCSA at their disposal could employ relational content analysis on the way profiles (people) react to and change one another on a massive (several thousand at once) scale in real-time!  People could be asked to not identify craters but instead read pieces of dialogue and describe their tone.  Such practices could be used to model new understandings of social behaviors in ways traditional qualitative or even other quantitative methods may never be able to achieve.  Scores of researchers could work together to assemble a gradually more accurate and comprehensive representation of a virtual aggregation of social realities (or systems).  Mayer and Puller didn’t go and interview students and ask if they had friends or acquaintances of another race nor did they distribute a survey on the topic.  They grabbed a near full-population size sample and simulated it—think the census only put together voluntarily.  Admittedly no SNS dataset is really to the point of ubiquity and the creation of models by only a few individuals is prone to error and bias but this very well could be the direction in which we’re headed nonetheless.

A question about knowledge and e-science (with rationale of why this question is important) for future research in the area

I’d like to bring this back to my original statement on the call for interdisciplinary collaborations.  If collaboratories based around Facebook-type datasets are an eventuality then how do we effectively encourage and enable social science and humanities researchers to motivate and drive them? Zhu and Li (2007) provide a partial answer to this question in expressing the need for social scientists and humanities experts to understand the mechanics, capabilities and limitations of technical systems.  This is a solid solution but seemingly skips a step: how do you get them to care in the first place?  Right now much of the “soft-science” world doesn’t even think along the lines of networked collaboration or distributed knowledge and those that do don’t typically view their work as compatible with it.  Some, especially those in fields steeped in postmodernism, have strong aversions to such techniques.

One answer might lay in future education programs.  If social science methods courses begin to include sections on e-social science in their repertoire then we can help to ensure future generations wield this knowledge.  We need teachers and innovative educational models for this and it’s not an answer to the current generation of researchers out there but it’s a start.

A review of Linda Flowers’ paper on intercultural rhetoric (2003) reminds me of yet another obstacle in the mission to reach more complete cognitive diversity.  The inclusion of disenfranchised groups or those without a voice at all must be forefront in this effort.  In this case the issue is twofold—there will still be power disparities evident in simulated social relations (ala Facebook) and some groups may be more excluded or squelched in these networks.  To effectively participate in virtually bounded (online) collaboratories also requires functional access (that is experiential and critical access, see Banks 2006), which is an encompassing issue of the digital divide.  Access to this information may also be an issue, if these studies are conducted on nearly everyone shouldn’t nearly everyone have access to their results?  Open Access would seem to be yet another imperative in the operation.

Regardless of these daunting questions and challenges e-social science and corresponding collaboratory efforts hold a great deal of promise and may yet be an untold future for the development and distribution of knowledge.

References
Adalbert Mayer and Steven L. Puller (2008). “The Old Boy (and Girl) Network: Social Network Formation on University Campuses.” Journal of Public Economics, vol. 92, issue 1-2, pages 329-347
Banks, Adam. (2006). Race, Rhetoric, and Technology. Mahwah, NJ: Lawrence Erlbaum Associates, Inc.
boyd, danah and Jeffrey Heer. (2006). “Profiles as Conversation: Networked Identity Performance on Friendster.” In Proceedings of the Hawai'i International Conference on System Sciences (HICSS-39), Persistent Conversation Track. Kauai, HI: IEEE Computer Society. January 4 - 7, 2006.
Bruce, B. C. (2008, June). Coffee cups, frogs, and lived experience. International Journal of Progressive Education, 4(2).
Dale, Angela. (2008). “Crossing Disciplinary Boundaries.” Presented at the 3rd ESRC Research Methods Festival, on July 1st 2008, Oxford, England.
Flowers, L. (2003). Talking across difference: Intercultural rhetoric and the search for situated knowledge. College Composition and Communication, 55(1), 38-68.
Haythornthwaite, C. (2006). Articulating divides in distributed knowledge practice. Information, Communication & Society, 9(6), 761-780.
Lampe, C., Ellison, N. and Steinfield, C., (2006). A Face(book) in the Crowd: Social Searching vs. Social Browsing. In Proceedings of the 2006 20th Anniversary Conference on Computer-Supported Cooperative Work (CSCW 2006), 167-170. New York, NE: ACM Press.
Surowiecki, James. (2005). The Wisdom of Crowds. New York: Random House Inc.
Zhu, Jonathan J.H. and Xiaoming Li. (2007). “How to Work Together? Interdisciplinary Collaboration in e-Social Science.” Presented at the e-Social Science Conference 2007, October 7-9, 2007, Ann Arbor.