Solomon Messing

Solomon Messing is a researcher and data scientist known for his work on how algorithms and social information embedded in new technologies affect the way people understand the political world. He was the founding Director of Pew Research Center's Data Labs, research scientist at Facebook and Twitter, chief scientist at Acronym, and is now Research Associate Professor at New York University.

Messing's work quantifying media polarization and filter bubbles was published in ''Science'' and has been influential in the field of political communication and sparked media commentary on the role of networks and algorithms in the media ecosystem. His work on how people understand election forecasting was the subject of public debate about the role of election forecasting in the democratic process and was cited by FiveThirtyEight's Politics Podcast as a reason for changing the forecast from percent change of winning to odds.

He also led the technical effort at Facebook to release perhaps the largest ever social media data set for research, which relied on a controversial technology, differential privacy, to protect data from malicious actors.

Messing earned his PhD in 2013 as well as a master's degree in Statistics from Stanford University. Provided by Wikipedia