Explore the latest research from Facebook

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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 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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June 9, 2020 Julian Runge, Steve Geinitz, Simon Ejdemyr
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Experimentation and Performance in Advertising: An Observational Survey of Firm Practices on Facebook

It is widely assumed that firms experiment with their online advertising to identify more profitable approaches to then increase their investment in more profitable advertising, increasing their overall performance. Generalizable evidence on the actual use of such experiment-based learning by firms is sparse. The study herein addresses this shortcoming – detailing the extent to which large advertisers are utilizing experimentation along with evidence on the benefits of doing so.
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June 8, 2020 Shankar Iyer, Justin Cheng, Nick Brown, Xiuhua Wang
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When Does Trust in Online Social Groups Grow?

To better test the potential causal pathways between trust and behaviors or group properties, we paired a two-wave longitudinal survey of 2358 participants in Facebook Groups with logged activity on Facebook. Using latent change score modeling, we examined how trust may predict changes in behavior or group properties and how behaviors and group properties may predict changes in trust.
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June 8, 2020 Fred Lin, Keyur Muzumdar, Nikolay Laptev, Mihai-Valentin Curelea, Seunghak Lee, Sriram Sankar
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Fast Dimensional Analysis for Root Cause Investigation in a Large-Scale Service Environment

In this paper we present a fast dimensional analysis framework that automates the root cause analysis on structured logs with improved scalability.
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