Publications

April 25, 2020

Conference on Human Factors in Computing Systems (CHI)

People compare themselves to one another both offline and online. The specific online activities that worsen social comparison are partly understood, though much existing research relies on people recalling their own online activities post hoc and is situated in only a few countries. To better understand social comparison worldwide and the range of associated behaviors on social media, a survey of 38,000 people from 18 countries was paired with logged activity on Facebook for the prior month.

Areas: Data Science Human Computer Interaction & UX

April 25, 2020

Conference on Human Factors in Computing Systems (CHI)

This paper compares data from ten self-reported Facebook use survey measures deployed in 15 countries (N = 49,934) against data from Facebook’s server logs to describe factors associated with error in commonly used survey items from the literature.

Areas: Data Science Human Computer Interaction & UX

April 20, 2020

International Conference on Control, Automation, and Robotics (ICCAR)

Solar-powered high-altitude long endurance aircraft that harvest and store solar energy can fly indefinitely if they are able to close a 24-hour energy cycle. Perpetual endurance is possible when energy consumption does not exceed energy storage.

Areas: Connectivity Data Science Economics & Computation

July 15, 2019

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.

Areas: Data Science Facebook AI Research Machine Learning

June 24, 2019

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.

Areas: Data Science

June 22, 2019

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.

Areas: Data Science Machine Learning

June 13, 2019

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.

Areas: Data Science Economics & Computation Facebook AI Research

June 1, 2019

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.

Areas: Data Science

May 19, 2019

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.

Areas: Connectivity Data Science

May 14, 2019

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.

Areas: Data Science Economics & Computation Facebook AI Research Machine Learning