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Solomon Messing

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Solomon Messing is a researcher and data scientist[1] 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,[1] research scientist at Facebook and Twitter,[2] chief scientist at Acronym,[3][4] and is now Research Associate Professor at New York University.

Messing's work quantifying media polarization and filter bubbles was published in Science[5] and has been influential in the field of political communication[6] and sparked media commentary on the role of networks and algorithms in the media ecosystem.[7][8][9][10] His work on how people understand election forecasting[11] was the subject of public debate about the role of election forecasting in the democratic process[12][13][14][15] and was cited by FiveThirtyEight's Politics Podcast[16] 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.[17][18]

Messing earned his PhD in 2013 as well as a master's degree in Statistics from Stanford University.[19]

Most cited peer-reviewed journal articles

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  • Bakshy E, Messing S, Adamic LA. Exposure to ideologically diverse news and opinion on Facebook. Science. 2015 Jun 5;348(6239):1130-2. cited 2441 times in Google Scholar[20]
  • Messing S, Westwood SJ. Selective exposure in the age of social media: Endorsements trump partisan source affiliation when selecting news online. Communication Research. 2014 Dec;41(8):1042-63. cited 925 times in Google Scholar [20]
  • Grimmer J, Messing S, Westwood SJ. How words and money cultivate a personal vote: The effect of legislator credit claiming on constituent credit allocation' American Political Science Review. 2012 Nov;106(4):703-19.cited 311 times in Google Scholar [20]
  • Bond R, Messing S. Quantifying social media’s political space: Estimating ideology from publicly revealed preferences on Facebook. American Political Science Review. 2015 Feb;109(1):62-78. cited 182 times in Google Scholar [20]

References

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  1. ^ a b "Q&A with Solomon Messing of Pew Research Center's Data Labs | Pew Research Center". Pew Research Center. Retrieved 2018-10-24.
  2. ^ "Solomon Messing". Facebook Research. Facebook. Archived from the original on 2019-06-11. Retrieved 11 June 2019.
  3. ^ Pasternack, Alex (2 November 2020). "This data expert helped Trump win. Now he's built a machine to take him down". Retrieved 1 December 2021.
  4. ^ "Election forecasts helped elect Trump in 2016. It could happen again in 2020". Yahoo News. Retrieved 2020-10-06.
  5. ^ Bakshy, Eytan; Messing, Solomon; Adamic, Lada (2015-05-07). "Exposure to ideologically diverse news and opinion on Facebook". Science. 348 (6239): 1130–2. Bibcode:2015Sci...348.1130B. doi:10.1126/science.aaa1160. ISSN 0036-8075. PMID 25953820. S2CID 206632821.
  6. ^ "Google Scholar". scholar.google.com. Retrieved 2018-10-25.
  7. ^ Manjoo, Farhad (2015-05-07). "Facebook Use Polarizing? Site Begs to Differ". New York Times. Retrieved 2018-10-24.
  8. ^ Mooney, Chris (May 7, 2015). "Facebook study says it's mainly your fault–not theirs–that you click on things you already agree with". Washington Post. Retrieved 2017-01-12.
  9. ^ Webb, Jonathan (2015-05-07). "Facebook studies news feed balance". BBC News. Retrieved 2018-10-24.
  10. ^ "Does Facebook's News Feed control your world view?". Retrieved 2018-10-24.
  11. ^ Westwood, Sean; Messing, Solomon; Lelkes, Yphtach (2018). "Projecting Confidence: How the Probabilistic Horse Race Confuses and Demobilizes the Public". SSRN Working Paper Series. doi:10.2139/ssrn.3117054. ISSN 1556-5068. S2CID 102488084. SSRN 3117054.
  12. ^ Bump, Philip. "Analysis | Clinton's Achilles' heel in 2016 may have been overconfidence". Washington Post. Retrieved 2018-10-24.
  13. ^ Kilgore, Ed. "Americans Don't Understand Election Predictions Expressed As Probabilities". Intelligencer. Retrieved 2018-10-24.
  14. ^ "Study Finds Election Forecasts Lower Voter Turnout". politicalwire.com. Retrieved 2018-10-24.
  15. ^ Uberti, David (2018-10-18). "Forecasting the midterms: Uncertainty with a chance of finger-pointing". Columbia Journalism Review. Retrieved 2018-10-24.
  16. ^ "Politics Podcast: What's So Wrong With Nancy Pelosi?". FiveThirtyEight. 2018-02-12. Retrieved 2018-10-24.
  17. ^ Flamini, Daniela (11 October 2019). "What can researchers find among the 32 million URLs Facebook just released to Social Science One?". Poynter. Poynter.
  18. ^ Aral, Sinan (Sep 14, 2021). The Hype Machine. Crown/Archetype. p. 276. ISBN 9780593240403.
  19. ^ "SMaPP Global". New York University Social Media and Political Participation Lab. New York University.
  20. ^ a b c d "Google Scholar".
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