Data Science

Gaining insights to deliver meaningful social interactions

Data scientists at Facebook conduct large-scale, global, quantitative research to gain deeper insights into how people interact with each other and the world around them.

Our findings directly inform decisions to improve people’s everyday experiences on Facebook, make it easier and more intuitive to use, and find ways to facilitate meaningful social interactions. Research efforts span a variety of disciplines, including computational social science, econometrics, operations research, market intelligence, survey science, and statistical computing. We employ a mixture of methods to accomplish our goals, including machine learning, field experiments, surveys, and information visualization. We also build scalable platforms for the collection, management, and analysis of data, and actively contribute our scientific findings to the academic research community.

For information about Core Data Science research at Facebook, visit the Core Data Science page.

Follow Data Science

Latest Publications

All Publications

EC - December 23, 2020

Matching Algorithms for Blood Donation

Duncan C. McElfresh, Christian Kroer, Sergey Pupyrev, Eric Sodomka, Karthik Abinav Sankararaman, Zack Chauvin, Neil Dexter, John P. Dickerson

CODE - November 20, 2020

Privacy-Preserving Randomized Controlled Trials: A Protocol for Industry Scale Deployment (Extended Abstract)

Mahnush Movahedi, Benjamin M. Case, Andrew Knox, Li Li, Yiming Paul Li, Sanjay Saravanan, Shubho Sengupta, Erik Taubeneck

CSCW - October 17, 2020

Country Differences in Social Comparison on Social Media

Justin Cheng, Moira Burke, Bethany de Gant

arXiv - October 9, 2020

Weights and Methodology Brief for the COVID-19 Symptom Survey by University of Maryland and Carnegie Mellon University, in Partnership with Facebook

Neta Barkay, Curtiss Cobb, Roee Eilat, Tal Galili, Daniel Haimovich, Sarah LaRocca, Katherine Morris, Tal Sarig

Explore Data Science

 

Automatic Alt-Text (AAT) allows screen reader users the ability to understand the content of most images (hopefully all images soon!) in News Feed.

 

The reach of Facebook friendships across European cities.

 

Calculating three and a half degrees of separation.

 

Researchers explored the dynamic of jobs running in families. This network visualization shows how much more likely a child of a parent in one profession is to choose another profession vs. someone from the general population.

 

Example social network showing exposure to diverse information on Facebook

 

Visualization of popular Indian politicians and political organizations for each state and their fan following across states. Visualization inspired by mbostock’s hierarchical edge bundling.

 

Figure shows different cities in India and the most prominent emotion among the posts from that city as they celebrated Independence Day.

Core Data Science at Facebook

3:09 | April 23, 2020

Open Source Projects

1:36 | August 16, 2019

Social Connectedness Index

1:11 | July 1, 2019

Social Good Projects

1:24 | July 1, 2019

Open Research Awards

View All Open Research Awards
February 24, 2021

Request for proposals on sample-efficient sequential Bayesian decision making

With this RFP, we hope to deepen our ties to the academic research community by seeking out innovative ideas and applications of Bayesian optimization that further advance the field. We are committed to open source and will help awardees make the products of this RFP available to the public as part of BoTorch.

Explore RFP Apply
Careers at Facebook Research
Want to solve some of the most challenging technology problems?