April 26, 2014

Incentives to Participate in Online Research: An Experimental Examination of “Surprise” Incentives

ACM Conference on Human Factors in Computing Systems (CHI)

In this work, we present four experiments examining how two different kinds of ‘surprise’ financial incentives affect the rate of participation in a longitudinal study when participants are initially solicited with either an appeal to intrinsic motivation to participate in research or one that also offers extrinsic financial incentives.

Andrew Tresolini Fiore, Coye Cheshire, Lindsay Shaw Taylor, G.A. Mendelsohn
April 26, 2014

Visually Impaired Users on an Online Social Network

ACM Conference on Human Factors in Computing Systems (CHI)

In this paper we present the first large-scale empirical study of how visually impaired people use online social networks, specifically Facebook. We identify a sample of 50K visually impaired users, a…

Shaomei Wu, Lada Adamic
April 26, 2014

Growing Closer on Facebook: Changes in Tie Strength Through Site Use

ACM Conference on Human Factors in Computing (CHI)

Scientists debate whether people grow closer to their friends through social networking sites like Facebook, whether those sites displace more meaningful interaction, or whether they simply reflect existing ties.

Moira Burke, Robert Kraut
April 11, 2014

Designing and Deploying Online Field Experiments

International World Wide Web Conference (WWW)

Online experiments are widely used to compare specific design alternatives, but they can also be used to produce generalizable knowledge and inform strategic decision making. Doing so often requires sophisticated experimental designs, iterative refinement, and careful logging and analysis.

Eytan Bakshy, Dean Eckles, Michael Bernstein
April 7, 2014

Personalized Collaborative Clustering

International World Wide Web Conference (WWW)

We study the problem of learning personalized user models from rich user interactions. In particular, we focus on learning from clustering feedback (i.e., grouping recommended items into clusters), wh…

Yisong Yue, Chong Wang, Khalid El-Arini, Carlos Guestrin
July 8, 2013

Families on Facebook

AAAI Conference on Weblogs and Social Media (ICWSM)

This descriptive study of millions of US Facebook users documents “friending” and communication patterns, exploring parent-child relationships across a variety of life stages and gender combinations.

Moira Burke, Lada Adamic, Karyn Marciniak
April 29, 2013

Quantifying the Invisible Audience in Social Networks

ACM Conference on Human Factors in Computing Systems (CHI)

When you share content in an online social network, who is listening? Users have scarce information about who actually sees their content, making their audience seem invisible and difficult to estimate. However, understanding this invisible audience can impact both science and design, since perceived audiences influence content production and self-presentation online.

Michael Bernstein, Eytan Bakshy, Moira Burke, Brian Karrer
April 27, 2013

Gender, Topic, and Audience Response: An Analysis of User-Generated Content on Facebook

ACM Conference on Human Factors in Computing Systems (CHI)

Although users generate a large volume of text on Facebook every day, we know little about the topics they choose to talk about, and how their network responds. Using Latent Dirichlet Allocation (LDA)…

Yi-Chia Wang, Moira Burke, Robert Kraut
April 1, 2013

Using Facebook after Losing a Job: Differential Benefits of Strong and Weak Ties

ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW)

Among those who have recently lost a job, social networks in general and online ones in particular may be useful to cope with stress and find new employment. This study focuses on the psychological an…

Moira Burke, Robert Kraut
July 21, 2011

Dimensions of Self-Expression in Facebook Status Updates

AAAI International Conference on Weblogs and Social Media (ICWSM)

We describe the dimensions along which Facebook users tend to express themselves via status updates using the semi-automated text analysis approach, the Meaning Extraction Method (MEM).

Adam D. I. Kramer, Cindy K. Chung
February 1, 2011

Social Capital on Facebook: Differentiating Uses and Users

ACM Conference on Human Factors in Computing Systems (CHI)

Though social network site use is often treated as a monolithic activity, in which all time is equally “social” and its impact the same for all users, we examine how Facebook affects social capital depending upon: (1) types of site activities, contrasting one-on-one communication, broadcasts to wider audiences, and passive consumption of social news, and (2) individual differences among users, including social communication skill and self-esteem.

Moira Burke, Robert Kraut, Cameron Marlow
February 1, 2010

Social Network Activity and Social Well-Being

ACM Conference on Human Factors in Computing Systems (CHI)

Previous research has shown a relationship between use of social networking sites and feelings of social capital. However, most studies have relied on self-reports by college students. The goals of the current study are to (1) validate the common self-report scale using empirical data from Facebook, (2) test whether previous findings generalize to older and international populations, and (3) delve into the specific activities linked to feelings of social capital and loneliness.

Moira Burke, Cameron Marlow, Thomas Lento
June 1, 2009

Feed Me: Motivating Newcomer Contribution in Social Network Sites

ACM Conference on Human Factors in Computing Systems

Social networking sites (SNS) are only as good as the content their users share. Therefore, designers of SNS seek to improve the overall user experience by encouraging members to contribute more content. However, user motivations for contribution in SNS are not well understood. This is particularly true for newcomers, who may not recognize the value of contribution. Using server log data from approximately 140,000 newcomers in Facebook, we predict long-term sharing based on the experiences the newcomers have in their first two weeks. We test four mechanisms: social learning, singling out, feedback, and distribution.

Moira Burke, Cameron Marlow, Thomas Lento