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Social Media Data to Be Used for Predictive Analytics in the U.S. Air Force

15 Feb 2018 5:36 PM | Kathy Doering (Administrator)

I have been reading more and more about how people are using large scale social network data for predictive analytics in healthcare and most recently, threats to our world.  In order to decide if this data is reliable enough to even consider this form of research, two studies are underway to find out. 

The Journal of Medical Internet Research recently published an article around using Facebook Groups in order to educate patients for type 2 diabetes mellitus (T2DM) and coronary heart disease (CHD). Facebook Groups are thought to be more convenient for patients who are undergoing rehabilitation than on-site groups. It lifts barriers of location and may encourage better peer- to- peer conversations and the exchange of important information. Of course, the Journal recommends healthcare professionals moderate initially and much more work needs to be done, however they feel it is a good option with some real potential for increased insight into both areas. Taking patient's day to day experiences in dealing with both illnesses and their exchange of information would offer some great data for medical professionals. 

Conrad Tucker, associate professor of engineering design and industrial engineering at Penn State, has received funding from the U.S. Air Force.  Tucker received $342,995 for the three-year project titled, “Transforming Large Scale Social Media Networks into Data-Driven, Dynamic Sensing Systems for Modeling and Predicting Real World Threats.”

Tucker explains, “So the major thrust of this project is to create algorithms that increase the reliability of the information that you can acquire from these publicly available sources.We live in an increasingly digitally connected world, and this connectivity actually presents challenges, like volatility,” said Tucker. “If one CEO’s tweet can send a stock’s price down billions of dollars, that is a huge threat to the company and its stakeholders. That is just one example of what we are looking to model with new algorithms that can analyze and predict such chaos.”

Within our connected world come risks and human behaviors that make it difficult to decipher real from fake information. I am very encouraged by these two initiatives to harvest social data for the good of society in a more controlled setting.  

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