Presentations

How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, not Engaged Argument, at University of Wisconsin-Madison, Monday, January 23, 2017:

This talk is based on this paper (forthcoming in the American Political Science Review), by me, Jennifer Pan, and Margaret Roberts, along with a brief summary of our previous work. Here's an abstract: The Chinese government has long been suspected of hiring as many as 2,000,000 people to surreptitiously insert huge numbers of pseudonymous and other deceptive writings into the stream of real social media posts, as if they were the genuine opinions of ordinary people. Many academics, and most journalists

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An Improved Method of Automated Nonparametric Content Analysis for Social Science, at Texas A&M Inaugural STATA Lecture, Thursday, January 19, 2017:

A vast literature in computer science and statistics develops methods to automatically classify textual documents into chosen categories. In contrast, social scientists are often more interested in aggregate generalizations about populations of documents --- such as the percent of social media posts that speak favorably of a candidate's foreign policy. Unfortunately, trying to maximize the percent of individual documents correctly classified often yields biased estimates of statistical aggregates. Fortunately, classification is neither a necessary nor even a desirable step in estimating

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Big Data is Not About the Data!, at Shanghai Jiao Tong University, Wednesday, January 4, 2017:

The spectacular progress the media describes as "big data" has little to do with the data.  Data, after all, is becoming commoditized, less expensive, and an automatic byproduct of other changes in organizations and society. More data alone doesn't generate insights; it often merely makes data analysis harder. The real revolution isn't about the data, it is about the stunning progress in the statistical and other methods of extracting insights from the data. I illustrate these points with a wide range of examples from research I've participated in from the quantitative social

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An Improved Method of Automated Nonparametric Content Analysis for Social Science, at New York University, Text as Data Speaker Series, Thursday, December 1, 2016:

A vast literature in computer science and statistics develops methods to automatically classify textual documents into chosen categories. In contrast, social scientists are often more interested in aggregate generalizations about populations of documents --- such as the percent of social media posts that speak favorably of a candidate's foreign policy. Unfortunately, trying to maximize the percent of individual documents correctly classified often yields biased estimates of statistical aggregates. Fortunately, classification is neither a necessary nor even a desirable step in estimating

Read more about An Improved Method of Automated Nonparametric Content Analysis for Social Science
How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, not Engaged Argument, at Pacific Information Operations Symposium, Honolulu, Tuesday, November 8, 2016:

This talk is based on this paper, by me, Jennifer Pan, and Margaret Roberts, along with a brief summary of our previous work. Here's an abstract: The Chinese government has long been suspected of hiring as many as 2,000,000 people to surreptitiously insert huge numbers of pseudonymous and other deceptive writings into the stream of real social media posts, as if they were the genuine opinions of ordinary people. Many academics, and most journalists and activists, claim that these so-called "50c party" posts

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Big Data is Not About the Data!, at University of Michigan, Friday, October 7, 2016:

The spectacular progress the media describes as "big data" has little to do with the data.  Data, after all, is becoming commoditized, less expensive, and an automatic byproduct of other changes in organizations and society. More data alone doesn't generate insights; it often merely makes data analysis harder. The real revolution isn't about the data, it is about the stunning progress in the statistical and other methods of extracting insights from the data. I illustrate these points with a wide range of examples from research I've participated in, including forecasting the

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Big Data is Not About the Data!, at Michigan State University, Thursday, October 6, 2016:

The spectacular progress the media describes as "big data" has little to do with the data.  Data, after all, is becoming commoditized, less expensive, and an automatic byproduct of other changes in organizations and society. More data alone doesn't generate insights; it often merely makes data analysis harder. The real revolution isn't about the data, it is about the stunning progress in the statistical and other methods of extracting insights from the data. I illustrate these points with a wide range of examples from research I've participated in, including 

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How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, not Engaged Argument, at Northeastern University, Monday, September 26, 2016:

This talk is based on this paper, by me, Jennifer Pan, and Margaret Roberts, along with a brief summary of our previous work. Here's an abstract: The Chinese government has long been suspected of hiring as many as 2,000,000 people to surreptitiously insert huge numbers of pseudonymous and other deceptive writings into the stream of real social media posts, as if they were the genuine opinions of ordinary people. Many academics, and most journalists and activists, claim that

Read more about How the Chinese Government Fabricates Social Media Posts for Strategic Distraction, not Engaged Argument
Big Data is Not About the Data! , at Venice, Italy, Friday, September 23, 2016:

The spectacular progress the media describes as "big data" has little to do with the data.  Data, after all, is becoming commoditized, less expensive, and an automatic byproduct of other changes in organizations and society. More data alone doesn't generate insights; it often merely makes data analysis harder. The real revolution isn't about the data, it is about the stunning progress in the statistical and other methods of extracting insights from the data. We illustrate these points with a wide range of examples from 

Read more about Big Data is Not About the Data!

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