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Meet IU Northwest’s Jie Wang, Ph.D.


Computer scientist revamps how data is analyzed in age of social media

Wednesday Nov 05, 2014


If you happen to walk by the office of Jie Wang, Ph.D., and catch her intently staring at online reviews of a product on Amazon.com, don’t immediately jump to the conclusion that she is frittering away her time.

Rather, the Assistant Professor of Computer Information Systems at Indiana University Northwest is entrenched in research. Research that could one day influence the way in which businesses and consumers approach and interpret the endless exchange of chatter in online communities.

On the surface, for example, most of us see online product reviews as just another convenience of our modern digital information society. When weighing whether to purchase a product or service, reviews posted online by fellow consumers are yet another handy tool at our disposal in the online universe.

Wang, however, views these conversations as user-generated content. Data that can be understood in new ways. Data that can be dissected to discover interesting patterns. Data that traditional computing hardware and software simply cannot process in real time. Really. Big. Data.

Simply put, “big data,” means mind-blowing varieties and volume of data that are constantly changing across the World Wide Web, so much so that most modern computers simply cannot handle the load. And in case you were wondering, yes, “big data” is a technical term.

Wang is collaborating with Axel Schulze-Halberg, Ph.D., IU Northwest Associate Professor of Mathematics. Having already published an article on computational quantum theory, now they are developing mathematical methods of analyzing big data in a multi-dimensional space. Eventually, Wang says the new methods they are developing to segment data and extract actionable knowledge will give rise to new software that can help professionals monitor the reputation of websites and determine the reliability of the comments that are constantly transmitted through online social communities. Assigning numerical thresholds to reviews, for instance, is one way of red-flagging a professionally written review that could really be a paid advertisement passed off as authentic.

In the introduction of her recently published article on the topic, Wang explains that people have begun to utilize online communities for advertising and promotions given the “fast transmission of information in web-based social networks.” She continued that with the growing use of such online review sites, which serve as virtual places “where people can post their experiences with a product or service and potential consumers can read these reviews and consider them in their purchasing decision,” incidents of online fraudulent reviews have increased.

Wang and Schulze-Halberg are studying the reliability patterns of online consumer reviews by solving a “simultaneous clustering” problem using high performance computing including the enhanced matrix factorization approach. This research project has been supported by IU and the Nvidia Corporation. She explains that data clustering is a “fundamental task which divides a set of objects into a number of groups while maximizing within-group similarity and minimizing between-group similarity.”

Ultimately, Wang’s research will help companies enhance the authenticity of user-generated content. This will lead to more trust in their products and services.

“This is an interdisciplinary project,” Wang said. “It is mathematics-, computer science- and social computing-based, because of the computational methods and social networking needed to group the data and the subsequent creation of computer programs used to execute the analysis. But this project has significant implications in the business world, as well, specifically in the fields of marketing and product promotion. This project will be helpful to business professionals who can use the findings to improve the customer experience.”

Educated at the Beijing University of Chemical Technology in electrical engineering prior to earning her doctorate in computer science, Wang began her career working with mechanical devices and electrical circuits that control chemical processes.

Believe it or not, she says going from chemical process control and instrumentation to social media isn’t all that big of a jump, from a systemic perspective.

“You can build a system running algorithms to control temperature. If the temperature is too high in a chemical reactor, for example, the system can bring it down to prevent an explosion,” Wang explained. “Think of this as a similar process, time-variant and fluctuant. There are constantly new reviews being posted and affecting the climate of the consumer landscape. Much like a gauge that is developed to regulate temperature, there is always new data coming in that will influence consumer behavior. This project provides a gauge of sorts for optimizing the trustfulness of social media conversations. There is a continuous need to update the results and incorporate new reviews.”

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