Wayne Xu

I am a media researcher specializing in data analytics. Prior to joining UMass Amherst, I was a postdoctoral researcher at Northeastern University’s Network Science Institute. I graduated with a Ph.D. in Communication from SUNY-Buffalo and a MA in Media Studies from University of Wisconsin-Milwaukee. I am originally from China and came to the States in 2009, with a plan to study digital media and their impact on social changes.

During the early days of my academic career, I was very enthusiastic about the empowering role of digital media in fostering civil society. I had a strong belief in citizen voices, crowdsourcing, and the marketplace of ideas. However, I have recently become wary of the peril of social media—with customization algorithms, our social media use can very likely create filter bubbles and echo chambers in which we are confined to like-minded crowds. This concern made me curious about how we bridge online groups divided over ideologies and politics.

My research is driven by such curiosity. It has two parts. I am interested in online communities and how people sort themselves into like-minded clusters. This interest led me to publish several articles and participate in three national grant projects about online opinions and social media engagement. Answering this question is hardly satisfactory, however. I believe the more important task is finding out strategies to connect divided constituents. That will be my research goal at UMass Amherst. These topics will also be covered in my upcoming class Social Media and Internet Communities.

I am truly blessed to have had an early exposure to data science. Since 2013, I’ve started learning and practice data mining and analytics. I am drawn to network analysis which provides rich insights into the ways that people and ideas are connected. Computational communication research is fairly recent. There is much to be done to promote the value of data analytics in media research. I don’t buy into the hype of big data. Instead, I concede that data-driven studies lack the sophistication that can only be complemented by qualitative investigation. So, my other goal at UMass Amherst is to align qualitative and quantitative scholars in our field.

I hope my research inspires not only my colleagues but my students as well. Drawing from my experience of teaching statistics at Northeastern, I sense that my key teaching mission at UMass Amherst is to help students gain data literacy. This does not necessarily mean that I want to teach the technical aspect of data. As our understanding of the world increasingly involves data (for example, data journalism, political polling, visualization, etc.), I’d like to see students become critical data consumers—those who can spot biases in the use of empirical data for arguments and decisions. One of the classes I will teach in the fall, 497DB Survey/Digital Behavioral Data, taps right into that.

You can find more information about my academic career at curiositybits.com.