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Year Published

100 Results

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.

By: 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…

By: 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.

By: 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.

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

Can cascades be predicted?

International World Wide Web Conference (WWW)

On many social networking web sites such as Facebook and Twitter, resharing or reposting functionality allows users to share others’ content with their own friends or followers. As content is reshared…

By: Justin Cheng, Lada Adamic, Alex Dow, Jon Kleinberg, Jure Leskovec
February 18, 2014

Help is on the Way: Patterns of Responses to Resource Requests on Facebook

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

Research suggests that social network sites can support social capital exchanges, which are often triggered by requests for assistance, such as seeking recommendations or asking for favors. In this paper, we study public status updates posted to Facebook in order to identify the pattern of responses to status updates that attempt to mobilize resources from the poster’s Facebook network.

By: Cliff Lampe, Rebecca Gray, Andrew Tresolini Fiore, Nicole Ellison
February 18, 2014

The Role of Founders in Building Online Groups

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

As a class, online groups are popular, but many die before they become successful. This research traced the fate of 472,231 new online groups. By the end of a 3-month observation period, 57% of the gr…

By: Robert Kraut, Andrew Tresolini Fiore
February 18, 2014

Romantic Partnerships and the Dispersion of Social Ties: A Network Analysis of Relationship Status on Facebook

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

A crucial task in the analysis of on-line social-networking systems is to identify important people — those linked by strong social ties — within an individual’s network neighborhood. Here we investig…

By: Lars Backstrom, Jon Kleinberg
December 16, 2013

Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising

Journal of Machine Learning Research (JMLR)

This work shows how to leverage causal inference to understand the behavior of complex learning systems interacting with their environment and predict the consequences of changes to the system. Such p…

By: Leon Bottou, Jonas Peters, Joaquin Quiñonero Candela, Denis Charles, Max Chickering, Elon Portugaly, Dipankar Ray, Patrice Simard, Ed Snelson
August 11, 2013

Uncertainty in Online Experiments with Dependent Data: An Evaluation of Bootstrap Methods

ACM Conference on Knowledge Discovery and Data Mining (KDD)

Many online experiments exhibit dependence between users and items. For example, in online advertising, observations that have a user or an ad in common are likely to be associated. Because of this, even in experiments involving millions of subjects, the difference in mean outcomes between control and treatment conditions can have substantial variance. Previous theoretical and simulation results demonstrate that not accounting for this kind of dependence structure can result in confidence intervals that are too narrow, leading to inaccurate hypothesis tests.

By: Eytan Bakshy, Dean Eckles