A data-driven approach to researching censorship and sensitive conversations on social media
October 19, 2016
Jason Q. Ng
Data Analyst Tumblr; Research Fellow, Citizen Lab
Like all nations, China has been profoundly affected by the emergence of the Internet, particularly new forms of social media which allow individuals themselves to be independent broadcasters of news. However the rise of "We Media" has also led to a corresponding rise in the filtering and blocking of online content in China. Identifying and explaining these disruptions comes with a host of challenges for researchers--ranging from technical ones like developing methodologies for tracking online censorship to non-technical ones like even defining what online censorship is.
In this talk, we'll look at a number of different ways online censorship can be defined as well as various data-driven techniques for revealing its occurrence in social media--as well as the various ways social media companies attempt to mask or justify it. However, just as important as identifying the mechanisms for how censorship is implemented is trying to understand the motivations for such behavior. Knowing both how and why online censorship occurs is key for not only academic researchers who hope to better understand content moderation and filtering practices, but it is essential information for the activists, journalists, and advocates who utilize such findings in their work.
About the speaker: Jason Q. Ng is currently a Research Fellow at the University of Toronto's Citizen Lab, Data Analyst at Tumblr, and author of Blocked on Weibo, a book on Chinese social media. He is also a research consultant at China Digital Times where he develops censorship monitoring tools and teaches a digital activism course at Columbia SIPA. His writing and research projects can be found at www.jasonqng.com.
Part of the Information Technology Policy Seminar series.