Explore the latest research from Facebook

All Publications

October 17, 2020 Justin Cheng, Moira Burke, Bethany de Gant
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Country Differences in Social Comparison on Social Media

Social comparison is a common focus in discussions of online social media use and differences in its frequency, causes, and outcomes may arise from country or cultural differences. To understand how these differences play a role in experiences of social comparison on Facebook, a survey of 37,729 people across 18 countries was paired with respondents’ activity on Facebook.
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October 9, 2020 Neta Barkay, Curtiss Cobb, Roee Eilat, Tal Galili, Daniel Haimovich, Sarah LaRocca, Katherine Morris, Tal Sarig
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Weights and Methodology Brief for the COVID-19 Symptom Survey by University of Maryland and Carnegie Mellon University, in Partnership with Facebook

The Facebook company is partnering with academic institutions to support COVID-19 research and to help inform public health decisions. Currently, we are inviting Facebook app users in the United States to take a survey collected by faculty at Carnegie Mellon University (CMU) Delphi Research Center, and we are inviting Facebook app users in more than 200 countries or territories globally to take a survey collected by faculty at the University of Maryland (UMD) Joint Program in Survey Methodology.
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September 22, 2020 Michael Bailey, Drew Johnston, Theresa Kuchler, Dominic Russel, Bogdan State, Johannes Stroebel
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The Determinants of Social Connectedness in Europe

We use aggregated data from Facebook to study the structure of social networks across European regions.
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August 24, 2020 Viet-An Nguyen, Peibei Shi, Jagdish Ramakrishnan, Udi Weinsberg, Henry C. Lin, Steve Metz, Neil Chandra, Jane Jing, Dimitris Kalimeris
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CLARA: Confidence of Labels and Raters

In this paper, we present CLARA (Confidence of Labels and Raters), a system developed and deployed at Facebook for aggregating reviewer decisions and estimating their uncertainty. We perform extensive validations and describe the deployment of CLARA for measuring the base rate of policy violations, quantifying reviewers’ performance, and improving their efficiency.
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August 22, 2020 Julian Mestre, Nicolas Stier
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Tight approximation for the minimum bottleneck generalized matching problem

We study a problem arising in statistical analysis called the minimum bottleneck generalized matching problem that involves breaking up a population into blocks in order to carry out generalizable statistical analyses of randomized experiments.
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July 25, 2020 Viet Ha-Thuc, Avishek Dutta, Ren Mao, Matthew Wood, Yunli Liu
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A Counterfactual Framework for Seller-Side A/B Testing on Marketplaces

We propose a counterfactual framework for seller-side A/B testing. The key idea is that items in the treatment group are ranked the same regardless of experiment exposure rate.
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July 20, 2020 Kevin Liou, Sean Taylor
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Variance-Weighted Estimators to Improve Sensitivity in Online Experiments

As companies increasingly rely on experiments to make product decisions, precisely measuring changes in key metrics is important. Various methods to increase sensitivity in experiments have been proposed, including methods that use pre-experiment data, machine learning, and more advanced experimental designs. However, prior work has not explored modeling heterogeneity in the variance of individual experimental users. We propose a more sensitive treatment effect estimator that relies on estimating the individual variances of experimental users using pre-experiment data.
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July 9, 2020 Jeremy Rapin, Pauline Bennet, Emmanuel Centeno, Daniel Haziza, Antoine Moreau, Olivier Teytaud
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Open Source Evolutionary Structured Optimization

Since many problems are efficiently solved using specific operators, Nevergrad therefore now enables using specific operators within generic algorithms: the underlying structure of the problem is user-defined information that several families of optimization methods can use and benefit upon. We explain how this API can help analyze optimization methods and how to use it for the optimization of a structured Photonics physical testbed, and show that this can produce significant improvements.
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July 6, 2020 Steven A. Sumner, Moira Burke, Farshad Kooti
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Adherence to suicide reporting guidelines by news shared on a social networking platform

We conducted a study to analyze adherence to suicide-reporting guidelines on news shared on social media and to assess how adherence affects reader engagement.
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June 29, 2020 Fred Lin, Antonio Davoli, Imran Akbar, Sukumar Kalmanje, Leandro Silva, John Stamford, Yanai Golany, Jim Piazza, Sriram Sankar
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Predicting Remediations for Hardware Failures in Large-Scale Datacenters

In this paper, we present a machine learning framework that predicts the required remediations for undiagnosed failures, based on the similar repair tickets closed in the past.
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