Research Area
Year Published

105 Results

December 19, 2014

Predicting the Quality of New Contributors to the Facebook Crowdsourcing System

Neural Information Processing Systems: Crowdsourcing and Machine Learning Workshop

We are interested in improving the quality and coverage of a knowledge graph through crowdsourcing features built into a social networking service. This work presents an approach to model user trust when prior history is lacking.

By: Julian Eisenschlos
August 24, 2014

Practical Lessons from Predicting Clicks on Ads at Facebook

International Workshop on Data Mining for Online Advertising (ADKDD)

Online advertising allows advertisers to only bid and pay for measurable user responses, such as clicks on ads. As a consequence, click prediction systems are central to most online advertising system…

By: Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yanxin Shi, Antoine Atallah, Stuart Bowers, Joaquin Quiñonero Candela
June 2, 2014

Rumor Cascades

AAAI Conference on Weblogs and Social Media (ICWSM)

Online social networks provide a rich substrate for rumor propagation. Information received via friends tends to be trusted, and online social networks allow individuals to transmit information to man…

By: Adrien Friggeri, Lada Adamic, Dean Eckles, Justin Cheng
June 2, 2014

Topic-based Clusters in Egocentric Networks on Facebook

AAAI Conference on Weblogs and Social Media (ICWSM)

Homophily suggests that people tend to befriend others with shared traits, such as similar topical interests or overlapping social circles. We study how people communicate online in term of conversati…

By: Lilian Weng, Thomas Lento
May 8, 2014

Joint Inference of Multiple Label Types in Large Networks

International Conference on Machine Learning (ICML)

We tackle the problem of inferring node labels in a partially labeled graph where each node in the graph has multiple label types and each label type has a large number of possible labels. Our primary…

By: Deepayan Chakrabarti, Stano Funiak, Jonathan Chang, Sofus Attila Macskássy
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