Research Area
Year Published

73 Results

April 25, 2020

Social Comparison and Facebook: Feedback, Positivity, and Opportunities for Comparison

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.

By: Moira Burke, Justin Cheng, Bethany de Gant

April 25, 2020

How Well Do People Report Time Spent on Facebook? An Evaluation of Established Survey Questions with Recommendations

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.

By: Sindhu Kiranmai Ernala, Moira Burke, Alex Leavitt, Nicole Ellison

April 20, 2020

Trajectory Optimization of Solar-Powered High-Altitude Long Endurance Aircraft

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.

By: Jack Marriott, Birce Tezel, Zhang Liu, Nicolas Stier

December 2, 2019

Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints

Workshop on Safety and Robustness in Decision Making at NeurIPS

In this paper, we propose a novel Thompson sampling algorithm for multi-outcome contextual bandit problems with auxiliary constraints. We empirically evaluate our algorithm on a synthetic problem. Lastly, we apply our method to a real world video transcoding problem and provide a practical way for navigating the trade-off between safety and performance using Bayesian optimization.

By: Sam Daulton, Shaun Singh, Vashist Avadhanula, Drew Dimmery, Eytan Bakshy

July 1, 2019

Multi-Dimensional Balanced Graph Partitioning via Projected Gradient Descent

International Conference on Very Large Databases (VLDB)

Motivated by performance optimization of large-scale graph processing systems that distribute the graph across multiple machines, we consider the balanced graph partitioning problem. Compared to most of the previous work, we study the multi-dimensional variant in which balance according to multiple weight functions is required.

By: Dmitrii Avdiukhin, Sergey Pupyrev, Grigory Yaroslavtsev

June 26, 2019

Pacing Equilibrium in First-Price Auction Markets

ACM Conference on Economics and Computation (EC)

In the isolated auction of a single item, second price is often preferable to first price in properties of theoretical interest. Unfortunately, single items are rarely sold in true isolation, so considering the broader context is critical when adopting a pricing strategy. In this paper, we show that this context is important in a model centrally relevant to Internet advertising: when items (ad impressions) are individually auctioned within the context of a larger system that is managing budgets, theory offers surprising support for using a first price auction to sell each individual item.

By: Vincent Conitzer, Christian Kroer, Debmalya Panigrahi, Okke Schrijvers, Eric Sodomka, Nicolas Stier, Chris Wilkens

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 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