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March 15, 2016

Social Hash: an Assignment Framework for Optimizing Distributed Systems Operations on Social Networks

USINEX Symposium on Networked Systems Design and Implementation (NSDI 2016)

We describe the social hash framework, which uses graph partitioning techniques to improve the performance of systems within Facebook. We highlight two applications: 1. how routing similar users to the same web cluster improves our cache performance, 2. how co-locating socially similar data on the same host improves the performance of data serving systems.

By: Alon Shalita, Brian Karrer, Igor Kabiljo, Arun Sharma, Alessandro Presta, Aaron Adcock, Herald Kllapi, Michael Stumm
March 8, 2016

Two-Axis Gimbal for Air-to-Air and Air-to-Ground Laser Communications

Facebook

For bi-directional links between high-altitude-platforms (HAPs) and ground, and air-to-air communication between such platforms, a hemispherical +30° field-of-regard and low-drag low-mass two-axis gimbal was designed and prototyped.

By: Amnon Talmor, Harvard K Harding Jr, Chien-Chung Chen
February 27, 2016

Modeling Self-Disclosure in Social Networking Sites

ACM CSCW

Social networking sites (SNSs) offer users a platform to build and maintain social connections. Understanding when people feel comfortable sharing information about themselves on SNSs is critical to a good user experience, because self-disclosure helps maintain friendships and increase relationship closeness.

By: Yi-Chia Wang, Moira Burke, Robert Kraut
February 27, 2016

How Blind People Interact with Visual Content on Social Networking Services

Computer-Supported Cooperative Work and Social Computing (CSCW)

In this paper, we explore blind people’s motivations, challenges, interactions, and experiences with visual content on Social Networking Services (SNSs). We present findings from an interview study of 11 individuals and a survey study of 60 individuals, all with little to no functional vision.

By: Violeta Voykinska, Shiri Azenkot, Shaomei Wu, Gilly Leshed
February 27, 2016

Once More with Feeling: Supportive Responses to Social Sharing on Facebook

Computer-Supported Cooperative Work and Social Computing

Using millions of de-identified Facebook status updates with poster-annotated feelings (e.g., feeling thankful or feeling worried), we examine the magnitude and circumstances in which people share positive or negative feelings and characterize the nature of the responses they receive.

By: Moira Burke, Mike Develin
February 27, 2016

What’s in a Like? Attitudes and Behaviors Around Receiving Likes on Facebook

Computer-Supported Cooperative Work and Social Computing

What social value do Likes on Facebook hold? This research examines people’s attitudes and behaviors related to receiving one-click feedback in social media.

By: Lauren Scissors, Moira Burke, Steve Wengrovitz
February 22, 2016

Information Evolution in Social Networks

Proc. WSDM'16

Social networks readily transmit information, albeit with less than perfect fidelity. We present a large-scale measurement of this imperfect information copying mechanism by examining the dissemination and evolution of thousands of memes, collectively replicated hundreds of millions of times in the online social network Facebook.

By: Lada Adamic, Thomas Lento, Eytan Adar, Pauline C. Ng
January 13, 2016

Social Networks and Labor Markets: How Strong Ties Relate to Job Transmission On Facebook’s Social Network

Journal of Labor Economics

This is an observational study of the social networks of 1.4 million US Facebook users who list an employer in their profile.

By: Laura K. Gee, Jason Jones, Moira Burke
January 7, 2016

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

2016 International Conference on Learning Representations

We stabilize Generative Adversarial networks with some architectural constraints and visualize the internals of the networks.

By: Alec Radford, Luke Metz, Soumith Chintala
December 15, 2015

Learning to Segment Object Candidates

NIPS

In this paper, we propose a new way to generate object proposals, introducing an approach based on a discriminative convolutional network. Our model obtains substantially higher object recall using fewer proposals. We also show that our model is able to generalize to unseen categories it has not seen during training.

By: Pedro Oliveira, Ronan Collobert, Piotr Dollar