Although policy makers aim to use the best available science in informing their decision making, experts can come to different conclusions in synthesizing existing scientific research. This project will use network analysis and text mining to develop a novel framework of tools and workflows to reveal potential sources of bias in expert literatures. The resulting framework will facilitate data-driven decision-making in a broad range of areas, including conservation, energy policy, healthcare, and sustainable development.
Project outputs will be beneficial for identifying risks in literature reviews, such as sponsor bias or the avoidance of citation of contradictory evidence, which will help reduce the spread of misinformation. This project is made possible by recent advances in network science and text mining methods, as well as the availability of abstracts, affiliation, citations, and funding data under suitable licenses for data science. The work is novel in bringing together complementary approaches that have not previously been combined: argumentation theory and the study of controversies; approaches for synthesizing evidence; and bibliometric and scientometric approaches for looking structurally at a field.
CAREER awards, administered under the Faculty Early Career Development Program, are the National Science Foundation’s most prestigious form of support and recognition for junior faculty who “exemplify the role of teacher-scholars through outstanding research, excellent education and the integration of education and research within the context of the mission of their organizations.” NSF project description
Do you use scientific literature? Would you like to provide feedback on tools for making sense of scientific literature? We seek citizen advisors who are interested in science-based decision-making. We are especially looking for feedback in four key areas: conservation, energy policy, healthcare, and sustainable development. People using science in any area are invited. If you are inclined, you are also welcome to try out our tools to analyze literature yourself. If you are interested in joining our citizen advisory board, please fill out this form or contact jodi@illinois.edu if you would like more information.
Dr. Jodi Schneider (PI), Associate Professor, School of Information Sciences, University of Illinois at Urbana Champaign, jodi@illinois.edu
Yuanxi Fu, research assistant & doctoral student, School of Information Sciences, University of Illinois at Urbana Champaign
Shashank Kambhatla, research assistant & undergraduate student, School of Information Sciences, University of Illinois at Urbana Champaign
Jana Sebestik, Interim Director, Office for Mathematics, Science, & Technology Education, College of Education, University of Illinois at Urbana Champaign
Hannah Smith, research assistant & doctoral student, School of Information Sciences, University of Illinois at Urbana Champaign, hksmith2@illinois.edu