Over the past few years, a number of influential studies have explored the relationship between Facebook use and social outcomes – with no work being more prominent or influential than the body of work constructed by Ellison, Lampe, and Steinfield.  Over a number of studies, the MSU team has robustly identified both main and interaction effects in the relationship between Facebook use and social capital.  One of the most prominent findings from this work concerns the relationship between Facebook use and bridging social capital – the idea that Facebook effectively brings you closer to your extended group of weak ties.

Building on this work, Christian decided to explore this relationship in more depth – by focusing on the relationship between Facebook interface element use and bridging social capital.  Since we know that Facebook use is associated with bridging social capital, we wanted to understand what types of uses are associated with bridging social capital.  Christian devised and implemented a survey that measured intensity of use of Facebook wall posts, status updates, direct messages, and chatting, and explored the relationship between these types of uses and social capital.  We found that wall posting, in particular, was associated with bridging social capital – which conditionally supported our hypothesis that “third party visible” communication (a unique affordance of SNS) is critical to the production of social capital.

You can read more about the precise details of the study, including the methodology and measurement techniques, by downloading the pre-print or viewing the slideshare of Christian’s talk.  A key take away from this research is the importance of publicly visible communication – for all of the criticism Facebook has taken over the way it handles the News Feed, the ability to broadcast to a bounded public proves inherently useful.  A secondary take away is the fact our gender control was significant, which indicates that males, who may feel more comfortable disclosing information publicly due to different attitudes about privacy, gain more from public disclosures than females.  NB: All limitations apply – self report, study done at one college, etc.  We welcome your comments and feedback!

A big thanks goes out to both Jane Brown and Paul Jones, who mentored this work as thesis advisors.  The work was supported by a grant from UNC’s Office of Undergraduate Research, and Christian’s travel was made possible by the UNC JOMC Knight Chair.  Thank you!  Finally, I’m happy to add Christian’s paper to a growing list of my students that have been published.  In January, work on social networks and privacy by my undergraduate student Jamila Thompson was published in the journal Computers in Human Behavior.  Earlier, Brett Bumgarner’s excellent work on motivations for Facebook use was published in First Monday.  I’m very proud of all these students!

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Abstract

This study explores the supportive and informational uses of social network sites that facilitate adaptation to transition. Adaptation to transition is a complex process contingent upon the management of stress associated with transition and general integration into the transitional environment. This study focuses on the transition to college, a major life event requiring integration into new settings, the negotiation of informational challenges, and the mastery of new roles and identities.

Social network sites represent a connective infrastructure within personal networks. Because social network sites are inherently connective, they afford a location for provision and receipt of social support during transition, and a site for the acquisition of information necessary for integration into the transitional environment. Drawing on data collected directly from a social network site that describes the networked activity of a freshman class over the course of their first semester at college, from a sample survey of freshmen with 1,198 respondents, and from 15 semi-structured interviews, this research has two primary components.

In the first component of analysis, I explore the structure and dynamics of socio-technical networks during transition. Using exponential random graph modeling, I identify the role and magnitude of preference, socio-demographic, and configuration factors in structuring socio-technical networks during transition. I then use an econometric framework to demonstrate that certain types of information sharing and profile change are associated with socio-technical network growth.

In the second component of analysis, I explore uses of social network sites that facilitate adaptation to transition. Using multiple regression and structural equation modeling, I demonstrate that supportive and social-informational uses of social network sites in transition exert a direct and mediated positive effect on overall adaptation. I then draw on interviews to explore supportive and informational uses of the social network site during transition, finding that social network sites are useful in pre-transition preparation, for social adaptation, and for academic support throughout the transition. Upon evaluation, I demonstrate that a social network site is a useful place to turn for the social and informational support that facilitates adaptation to transition.

• Dr. Gary Marchionini, SILS, UNC-Chapel Hill
• Dr. Deborah Barreau, SILS, UNC-Chapel Hill
• Dr. danah boyd, Microsoft Research
• Dr. Sri Kalyanaraman, JOMC, UNC-Chapel Hill
• Paul Jones, JOMC and SILS, UNC-Chapel Hill

It was a great honor to work with my committee, and I appreciate their guidance in my work.  There are still a few edits remaining on my dissertation, and I will post it when I submit the final copy to the graduate school.  In the meantime, I’ve posted the slides from my talk as a Slideshare.

I’m happy to invite you to my dissertation defense, December 8 at 10AM, in Manning 014. This is a small room so seating might be limited. Full information is available on the SILS website.

I hope to post my dissertation in a few weeks. I will share it here when it is ready for public viewing.

Title:
Socio-Technical Support Networks During Life Transition

Abstract:
Modern life is characterized by transition.  Completing education, moving between jobs and residential relocation are examples of the transitions that challenge us, enable personal growth, and facilitate the construction of our life stories.  Successful adaptation to transition is a function of social-informational processes.  During a transition, individuals are challenged to make sense of their transitional environment, while developing socially supportive resources that aid in transition.  Large-scale adoption of social media, and resultant tightly-coupled mediated sociality has the potential to facilitate life transition; through social media, individuals are able to answer situationally relevant information needs, while drawing on extended support networks.  Using observational data collected during one such transition – the transition to college – this project explores social network site information practices during life transition.  In particular, I explore the dynamics of network configuration during the early stages of transition, identifying factors relevant to the assemblage and growth of socio-technical support networks.  I then explore the outcomes of social network site use during transition, identifying information behaviors associated with adaptation to transition.

I believe the talk will be at 3PM.  I’m looking forward to visiting the Department of Informatics, as well as meeting with faculty and students.  I’ll update the post with location information as we get closer to the date.

Alessandro’s team has extensive experience studying privacy in online social networks.  Alessandro and Ralph Gross wrote one of the earliest (and most cited) Facebook privacy papers: Imagined Communities: Awareness, Information Sharing, and Privacy on the Facebook. Last summer, the team published a truly head-turning study, showing that information gleaned from social network profiles could be used to predict social security numbers.  Most recently, Alessandro’s work was featured in Jeffrey Rosen’s New York Times Magazine article The Web Means the End of Forgetting.

I look forward to building on my current areas of research – privacy, identity and support in social networks – while being exposed to new opportunities and new challenges at CMU.  Speaking of challenges, the next challenge is a dissertation defense (later this fall) and then a move to Pittsburgh.  It has been a while since I’ve been to Pittsburgh, so I’m open to advice!

Because of this sanctioning system, we can make a reasonable inference that items that have been taken off of Craigslist have been sold.  The items that have short lifespans on Craigslist are desirable – they are a good value, priced properly – and those with long lifespans are either unwanted or improperly priced.  I’ve recently been in the market for a used car (cough, a minivan), so I’ve been collecting information about the cars offered on Craigslist and their lifespans on the service. By looking at prices and lifespans (and a few other variables), can we automatically identify cars that offer the greatest value?

What follows are some charts from a simple survival analysis of the last 30 days of Honda Odyssey sales on Craigslist in Raleigh/Durham.  The de-duped dataset includes 55 cars (out of about 130 posts). Before you read much into the data, many of the variables I explored (mileage, model year, etc.) weren’t significant predictors of “hazard” (that is, sale). If you were able to get this data on a larger scale, it does seem likely you’d be able to identify patterns of value. That said, there is a lot of randomness is a car’s quality once it has been driven, so the value of such a model-based approach would only be in prioritizing potentially under-priced cars.

n.b.: You could also do this sort analysis on want-ads. Want-ads have a great sanctioning system, as it is pointess to pay for an ad after you’ve sold your car.

p.s.: Perhaps what is charming about Craigslist is that there isn’t any meaningful historical data. This likely generates more variability in price, leading to the perception that you can find great deals (which you can!).

However, we’ve gotten feedback from translators and users in other countries that translations wind up being too confusing when people have not specified a sex on their profiles. People who haven’t selected what sex they are frequently get defaulted to the wrong sex entirely in Mini-Feed stories. For this reason, we’ve decided to request that all Facebook users fill out this information on their profile.

Just today, I discovered (via the R Bloggers news feed) an video on the use of R in corporations like Google and Facebook.  The representative of the Facebook data team talked about some exploratory data analysis they did in 2007.  The finding?  “If a user comes on more than once and is willing to give Facebook a very basic piece of information – their gender – that seems to be the strongest predictor of whether they will stay on the site.”

I’m not looking to stir up any controversy.  Rather, I think it is an interesting example of analytics-based development, of research informing design.  Of course, the challenge of translating research into practice is immense.  Are there critical differences between individuals that share gender and those that don’t?  Did a forced gender-selection process invalidate the predictive model?  Was the controversy over gender selection worth the predicted benefit?  Perhaps Facebook’s 500 million users owe more to gender selection than we can imagine.

Anyway, the video has some age on it, but I did enjoy hearing about Facebook’s use of R (the other analytic examples provided are cited in the “Maintained Relationships on Facebook” report, plus there are a few ICWSM papers, I believe).  You can find the full video here (doesn’t look like embed is supported).

Update: Please see the response from Cameron Marlow, Facebook Data Team lead, in the comments. Cameron provides great context for this finding.

Recently, I ran a survey of first-year students at UNC that tested preferences toward compensation.  The survey offered a dual-tier lottery compensation: Participants were entered to win an iPod touch or their choice of three gift certificates (See [2] for more on dual-tier incentives).  The three gift card choices were iTunes, Amazon, or a popular on-campus cafe, in the amount of ten dollars.  Response to the survey was good, by email-solicitations standards, at 31% (n~1200).  Males were slightly underrepresented, as is commonly the case.

So, what gift cards did my students prefer?  Clearly, the students preferred gift cards to iTunes (n=442) and Amazon (n=442) over the local cafe (n=131).  And we don’t really need any significance tests to see that the difference between iTunes and Amazon is a wash (p=.8406).

When conducting surveys, we’re not always interested in a large homogeneous population.  Sometimes we’re interested in sub-populations, such as certain genders, ages, or ethnicities.    Breaking the perferences out by gender, visual inspection indicates that female students prefer iTunes over Amazon, while male students prefer Amazon over iTunes.  Since neither population comes close to preferring the local cafe, I will focus on the difference between iTunes and Amazon for the rest of the analysis (i.e. drop the people who prefer the Local Cafe).

Of the students that selected Amazon or iTunes, we see that 53% of female students prefer iTunes, 47% Amazon.  Of males, 58% prefer Amazon, 42% iTunes.  The Chi-square test indicates a relationship between gender and preference (p=.001), and within-gender Chi-square goodness of fit tests indicate that while the female student preference difference is insignificant (.0922), the male preference towards Amazon is significant (p=.0064).

To test some higher order interactions, I employed a logistic regression model to test the effects of gender and a few other covariates.  First, since much of my sample is from NC, I tested to see if NC residency might contribute towards a preference.  In this model, gender remained significant, but NC residence was not significant (p=.828).  Next, looked to see if GPA might be a factor in preference.  Gender remained significant, and GPA’s p-value was low (p=.081), but not close to significance (directionality was higher GPA’s towards Amazon).

In the last two models, I looked at ethnicity and age.  In the ethnicity model, gender is significant, and only one ethnicity is significant.  Compared to other ethnicities, students who self-report as Asian demonstrate a preference towards Amazon (OR=.158, p=.000).  With age, gender again remained significant, but 19 year old students (compared to 18 year old students) seem to prefer iTunes (OR 1.49, p=.004).  Notably, a gender by age interaction was not significant, however.

To briefly review, it seems that among my population, the anecdotal preference towards iTunes is just that: anecdotal.  This is good news for me, because it is much more complicated to process iTunes gift cards than Amazon gift cards.  Some final notes: This is not really a proper experiment – such an experiment would use completely randomized solicitation.  Also, the presence of the third category (Local Cafe) is potentially troubling if being a fan of a Local Cafe also correlates to, say, being an iTunes fan or an Amazon fan.  Caveat emptor, blog post, not peer reviewed, etc.

1.  I don’t have a citation for this, but I do monitor to a number of email lists that frequently offer research solicitations.  YMMV.

2. See Li, Kaiwen (2006).  Student Preference for Survey Incentive.  UC Davis Student Affairs Research & Information Tech Report.

Finally, I promise that Amazon has not compensated me in any way, say, by sending me a bunch of gift certificates or a Nikon 12-24mm DX lens or anything like that.

The lecture by Carter Butts will be of particular interest to individuals looking to use R for social network analysis.  Butts is the author of a number of network analysis packages for R (many of which come bundled in the amazing statnet package).

Network Analysis with statnet for Individual, Organizational, and International Relations Applications by Carter Butts, University of California-Irvine

• Lecture Part 1
• Lecture Part 2

Advanced Network Analysis by Matthew O. Jackson, Stanford University

• Lecture Part 1
• Lecture Part 2

If you find these materials useful, you might also wish to check out Steve Goodreau and David Hunter’s tutorial Advanced Social Network Analysis Using R and statnet available at the Complexity and Social Networks blog.