Research Area
Year Published

121 Results

July 15, 2019

Searching for Communities: a Facebook Way

ACM SIGIR Conference on Research and Development in Information Retrieval

Giving people the power to build community is central to Facebook’s mission. Technically, searching for communities poses very different challenges compared to the standard IR problems.

By: Viet Ha-Thuc, Srinath Aaleti, Rongda Zhu, Nade Sritanyaratana, Corey Chen

June 24, 2019

When can overambitious seeding cost you?

Applied Network Science

Here, we extend earlier work by showing that the costs of overambitious seeding also appear in more traditional threshold models of collective behavior, once the possibility of permanent abandonment of the product is introduced. We further demonstrate that these costs can be mitigated by using conservative seeding approaches besides those that we explored in the earlier paper. Synthesizing these results with other recent work in this area, we identify general principles for when overambitious seeding can be of concern in the deployment of social products.

By: Shankar Iyer, Lada Adamic

June 22, 2019

HackPPL: A Universal Probabilistic Programming Language

MAPL at PLDI

This paper overviews the design and implementation choices for the HackPPL toolchain and presents findings by applying it to a representative problem faced by social media companies.

By: Jessica Ai, Nimar S. Arora, Ning Dong, Beliz Gokkaya, Thomas Jiang, Anitha Kubendran, Arun Kumar, Michael Tingley, Narjes Torabi

June 13, 2019

Discovering Context Effects from Raw Choice Data

International Conference on Machine Learning (ICML)

Many applications in preference learning assume that decisions come from the maximization of a stable utility function. Yet a large experimental literature shows that individual choices and judgements can be affected by “irrelevant” aspects of the context in which they are made. An important class of such contexts is the composition of the choice set. In this work, our goal is to discover such choice set effects from raw choice data.

By: Arjun Seshadri, Alexander Peysakhovich, Johan Ugander

June 1, 2019

Top Challenges from the first Practical Online Controlled Experiments Summit

SIGKDD Explorations

To understand the top practical challenges in running OCEs at scale, representatives with experience in large-scale experimentation from thirteen different organizations (Airbnb, Amazon, Booking.com, Facebook, Google, LinkedIn, Lyft, Microsoft, Netflix, Twitter, Uber, Yandex, and Stanford University) were invited to the first Practical Online Controlled Experiments Summit. All thirteen organizations sent representatives.

By: Somit Gupta, Ronny Kohavi, Diane Tang, Ya Xu, Reid Andersen, Eytan Bakshy, Niall Cardin, Sumitha Chandran, Nanyu Chen, Dominic Coey, Mike Curtis, Alex Deng, Weitao Duan, Peter Forbes, Brian Frasca, Tommy Guy, Guido W. Imbens, Guillaume Saint Jacques, Pranav Kantawala, Ilya Katsev, Moshe Katzwer, Mikael Konutgan, Elena Kunakova, Minyong Lee, MJ Lee, Joseph Liu, James McQueen, Amir Najmi, Brent Smith, Vivek Trehan, Lukas Vermeer, Toby Walker, Jeffrey Wong, Igor Yashkov

May 19, 2019

Facebook Disaster Maps: Aggregate Insights for Crisis Response & Recovery

Conference on Information Systems for Crisis Response and Management (ISCRAM)

In this paper, we describe the data and methodology that power Facebook Disaster Maps. These maps utilize information about Facebook usage in areas impacted by natural hazards, producing aggregate pictures of how the population is affected by and responding to the hazard. The maps include insights into evacuations, cell network connectivity, access to electricity, and long-term displacement.

By: Paige Maas, Shankar Iyer, Andreas Gros, Wonhee Park, Laura McGorman, Chaya Nayak, Alex Dow

May 14, 2019

Online Learning for Measuring Incentive Compatibility in Ad Auctions

The Web Conference

In this paper we investigate the problem of measuring end-to-end Incentive Compatibility (IC) regret given black-box access to an auction mechanism. Our goal is to 1) compute an estimate for IC regret in an auction, 2) provide a measure of certainty around the estimate of IC regret, and 3) minimize the time it takes to arrive at an accurate estimate.

By: Zhe Feng, Okke Schrijvers, Eric Sodomka

May 4, 2019

Understanding Perceptions of Problematic Facebook Use

ACM Conference on Human Factors in Computing Systems (CHI)

While many people use social network sites to connect with friends and family, some feel that their use is problematic, seriously affecting their sleep, work, or life. Pairing a survey of 20,000 Facebook users measuring perceptions of problematic use with behavioral and demographic data, we examined Facebook activities associated with problematic use as well as the kinds of people most likely to experience it.

By: Justin Cheng, Moira Burke, Elena Goetz Davis

May 4, 2019

When Do People Trust Their Social Groups?

ACM CHI Conference on Human Factors in Computing Systems

Trust facilitates cooperation and supports positive outcomes in social groups, including member satisfaction, information sharing, and task performance. Extensive prior research has examined individuals’ general propensity to trust, as well as the factors that contribute to their trust in specific groups. Here, we build on past work to present a comprehensive framework for predicting trust in groups.

By: Xiao Ma, Justin Cheng, Shankar Iyer, Mor Naaman

April 12, 2019

Presto: SQL on Everything

IEEE International Conference on Data Engineering (ICDE)

Presto is an open source distributed query engine that supports much of the SQL analytics workload at Facebook. Presto is designed to be adaptive, flexible, and extensible.

By: Raghav Sethi, Martin Traverso, Dain Sundstrom, David Phillips, Wenlei Xie, Yutian "James" Sun, Nezih Yigitbasi, Haozhun Jin, Eric Hwang, Nileema Shingte, Christopher Berner