ALUMNI FOCUS
One to watch
Macquarie alumnus Stephen Wan is one of the faces of the new national campaign launched by CSIRO, called CSIROseven, which looks at the innovative work of seven prominent young scientists.
Stephen’s specialty is in digital technologies, and in the campaign he discusses how his work draws together computer science, social media, and human language to generate helpful insights from the millions of public opinions, likes and re-tweets shared online each minute.
“Every minute millions of people share content on their social media – articles, videos, photos, personal opinions and more,” he says.
“It is a vast sea of data that grows at an overwhelming rate, so much so that it is impossible to read and categorise by hand, yet it may have the potential to provide game-changing insights,” he continues, adding that these insights could help us understand social factors that contribute to health issues like depression and obesity, which are often discussed online, for example.
“These insights could help improve the health system.”
He says that he recently helped the State Library of New South Wales to collect public discussions on the NSW state election to better understand why people voted the way that they did.
“Where usually we would have to wait for someone to write articles discussing the public opinion on election matters, our program was able to capture this collective voice in the discussion in real time.
“Our program could show us the information (news links) that were being shared and with what frequency, highlighting the different topics and their importance to us as a society.”
Most importantly he says that by tracking this information over time, and watching how opinions and media interact and affect each other it will help researchers understand just what impact this commentary has on voting behaviour.
It all comes down to language, Stephen says.
“One of the things that makes language so interesting is the way in which we are creative with it – a word can have a number of different meanings, and this may depend on factors such as the age, gender, location and cultural context of the person using it.
“This presents us with a unique challenge: to design automated programs that grasp those subtleties. For example, how do you write a program to detect something as complex and evolving as human sentiment?
“These are the kinds of questions that we work towards answering each and every day.”