M.S. (Master of Science)
Department of Computer Science
Endeavors to identify valuable research involve the factors of discovery, comprehensibility, and reproducibility. The purpose of this study is to assist scholars in finding research that is both promising and of high quality. I explain how we can approach the problem of reproducibility in relation to scholarly articles and propose gauging the public understanding of science as a way to determine the comprehensibility of given research articles. Additionally, I explain how the concept of long-term social media impact supports the discovery of scholarly articles likely to be impactful even with the passage of time. I build and describe machine-learning models that predict (1) whether or not a given scholarly article is reproducible (reproducibility), (2) the degree to which the scholarly article is understandable (public understanding of science), and (3) the degree to which the social media attention an article receives changes five years after publication (long-term social media impact). The features selected for these models were derived from research articles and social media indicators (i.e., altmetrics). These features encode linguistic information describing the article and structural details and meta-information indicators about the article.
Akella, Akhil Pandey, "Using Machine Learning Models to Discover Promising Research" (2019). Graduate Research Theses & Dissertations. 6780.
Northern Illinois University
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