Published 05/20/15

John T. Quinn: Parsing data about college majors, higher ed and the twitterverse

John QuinnJohn T. Quinn, Ph.D., whose research is wide-ranging and diverse, made Bryant a priority even during his spring 2014 sabbatical. In a paper titled “Cluster Analysis and Persistence in College Majors,” Quinn, Professor of Mathematics, and  two co-authors evaluated data to determine the percentage of Bryant students who remain in their declared major.

The data revealed that 56 percent of those declaring a major as incoming first-year students graduated in that same major; the two highest percentages for specific majors were 70 percent of accounting majors and 63 percent of math majors.

"Do people re-tweet information; if so, which topics are most re-tweeted? Is Twitter an efficient way to spread information?"

Another focus was his work as a co-investigator for a Bryant Advanced Applied Analytics Center research grant, “Analysis of the Social Structure or Information Spread in Diverse Fan-Based Communities in Twitter.” The project is a “family affair” – with contributions from Quinn’s sons, Christopher, a Purdue University professor, and Matthew, a Williams College student – and his Bryant colleague, Alan Olinsky, Ph.D.  

The foursome is now “parsing the Twitter data from some 15 colleges and universities, including Bryant,” said Quinn. “We’re looking at a ‘social network graph’ – who follows these individual schools, who they are following, and who follows them.”

Quinn, who does not tweet but follows Bryant’s Twitter postings, explains, “We want to see what information is being put out there. Do people re-tweet information; if so, which topics are most re-tweeted? Is Twitter an efficient way to spread information? Is there any interconnectedness among followers of schools?”

Quinn reported that early this summer the group plans to submit an invited chapter to the book Advances in Business and Management Forecasting, Volume 11. The abstract notes: “Online social networks are increasingly important venues for businesses to promote their products and image. However, information propagation in online social networks is significantly more complicated compared to traditional transmission mediums such as newspaper, radio, and television. In this chapter, we will discuss research on modeling and forecasting diffusion of virally marketed content in social networks.”

During his sabbatical, Quinn co-presented a paper, to be submitted for publication, addressing the economic impact of missing or flawed Medicare reimbursement data and co-authored, with his sons, a book chapter that identifies problems in accessing, storing, and analyzing social network data.