Assistant Professor Jana Diesner and two GSLIS doctoral students began their work on one of the University of Illinois's first grants from leading international brewer Anheuser-Busch (AB) InBev. The project team, including Diesner as principal investigator and doctoral students Jinseok Kim and Shubhanshu Mishra, will use data mining and social network analysis techniques advance techniques for assessing the impact of information disseminated on social media. They met with AB InBev representatives at the company’s St. Louis headquarters in August to get a deeper understanding of the processes, technologies and data used at their data analytics and customer services management unit.
Diesner is an expert in network analysis and has developed a tool that analyzes data culled from a variety of sources, including social media, in order to reveal connections between social stakeholders and the content that they are produce and share.
“We are developing cutting-edge methods and knowledge at the nexus of natural language processing, network analysis, and machine learning to improve and advance the status quo of actionable social listening,” Diesner explained. “We are assessing the impact of information shared by this organization in an empirical, rigorous, and scalable fashion. The close collaboration with AB InBev provides us with access to real-world, large-scale data and metadata as well as subject matter expertise that can help us to evaluate the performance and usefulness of the computational solutions that we are developing.”
Titled, “Socio-Technical Data Analytics for Improving Impact and Impact Assessment,” the project is expected to continue through April 2015. Along with one other recently-awarded Illinois grant, it cements a developing partnership between Illinois and AB InBev, which established a permanent office in the University’s Research Park last fall.
Diesner is an assistant professor at the iSchool at the University of Illinois at Urbana-Champaign. She earned her PhD from Carnegie Mellon University, School of Computer Science, in the Computation, Organizations and Society (COS) Program. Diesner conducts research at the nexus of network science, natural language processing and machine learning. Her research mission is to contribute to the computational analysis and better understanding of the interplay and co-evolution of information and the structure and functioning of socio-technical networks. She develops and investigates methods and technologies for extracting information about networks from text corpora and considering the content of information for network analysis. In her empirical work, she studies networks from the business, science and geopolitical domain. She is particularly interested in covert information and covert networks.
