Sinan Aral


David Austin Professor of Management
Professor of Information Technology and Marketing

 

Sinan Aral

Sinan Aral is the David Austin Professor of Management at MIT, where he is a Professor of IT & Marketing, and Professor in the Institute for Data, Systems and Society where he co-leads MIT’s Initiative on the Digital Economy.

He was the chief scientist at SocialAmp, one of the first social commerce analytics companies (until its sale to Merkle in 2012), and at Humin, a social platform that the Wall Street Journal called the first “Social Operating System” (until its sale to Tinder in 2016). He is currently a founding partner at Manifest Capital and on the Advisory Board of the Alan Turing Institute, the British National Institute for Data Science, in London. Sinan was the scholar-in-residence at the New York Times R&D Lab in 2013, and has worked closely with Facebook, Twitter, Snap, AirBnB, Yahoo, Jet.com, Microsoft, IBM, Intel, Cisco, Oracle, SAP, and many other leading Fortune 500 firms on realizing business value from big data analytics, social media, and IT investments.

Sinan’s research has won numerous awards including the Microsoft Faculty Fellowship, the PopTech Science Fellowship, an NSF CAREER Award, and a Fulbright Scholarship. In 2014, he was named one of the “World’s Top 40 Business School Professors Under 40” by Businessweek.

Sinan is a Phi Beta Kappa graduate of Northwestern University, holds Master’s degrees from the London School of Economics and Harvard University, and received his PhD from MIT.

He enjoys cooking, skiing, and telling jokes about his own cooking and skiing. His most recent hobby is learning from his four-year-old son. You can find Sinan on Twitter @sinanaral.


Faculty Media

  • MIT Has a Plan to Measure the Impact of 2016 Election Interference

    Sinan Aral discusses the four-point strategy to understand the impact of fake news and social-media manipulation.


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  • Social Media Sharing and Online News Consumption

    Newspapers and other content producers are all desperate to know whether social media sharing creates more viewership or cannibalizes it. In this research paper, Sinan Aral and Michael Zhao...


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  • The Truth About False News with Sinan Aral, MIT

    False news is big news. Barely a day goes by without a new development about the veracity of social media, foreign meddling in U.S. elections, or questionable science.


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  • MIT Sloan Experts Series – Sinan Aral: The Truth About Fake News

    Sinan Aral, David Austin Professor of Management at MIT Sloan and author of the forthcoming book, “The Hype Machine,” discusses insights from his latest research with co-researchers Soroush...


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  • [Innovation@Work Blog] Coveted Teaching Awards Presented to MIT Sloan Faculty

    As well-respected experts throughout the world, MIT Sloan faculty are certainly used to getting awards and accolades for their achievements.


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  • Running May Be Socially Contagious

    Can our workouts be shaped by what our friends do?


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  • Exercise Can Be Contagious

    New analysis of the running habits of about 1.1 million people reveals that exercise is indeed contagious — though its communicability depends on who’s spreading it.


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  • If the Ad-driven Model for Media is Broken, What Will Fix It?

    Professor Sinan Aral discusses micropayments as a new form of online advertising.


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  • Market Experimentation Meets Big Data

    Business leaders are continuously seeking out new and improved ways to drive decisions and meet consumer needs. Many companies are conducting experiments with online users every minute every minute...


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  • Digital Communication Revolution

    Sinan Aral discusses research on the power of social media at UD talk.


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Contact Information

Office: E62-364
Phone: (617) 324-7535
Fax:
Email: sinana@MIT.EDU
Website: http://web.mit.edu/sinana/www/
Support Staff
Name: Allie McDonough
Phone: (617) 324-6710

Teaches In

Digital Marketing Analytics (self-paced online) Dec 4, 2019-Feb 4, 2020 | Feb 5-Mar 24, 2020 | Apr 1-May 19, 2020 | Jun 3-Jul 21, 2020 | Aug 5-Sep 22, 2020 | Sep 30-Nov 17, 2020 | Nov 25, 2020-Jan 26, 2021

Machine Learning in Business (self-paced online) Jan 29-Mar 17, 2020 | Mar 18-May 5, 2020 | May 13-Jun 30, 2020 | Jul 1-Aug 18, 2020 | Sep 2-Oct 20, 2020 | Oct 28-Dec 15, 2020