Predicting citations from mainstream news, weblogs and discussion forum
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Mohan Timilsina, Brian Davis, Mike Taylor, and Conor Hayes. 2017. Predicting Citations from Mainstream News, Weblogs and Discussion Forums. In Proceedings of WI ’17, Leipzig, Germany, August 23-26, 2017, 8 pages. https://doi.org/10.1145/3106426.3106450
The growth in the alternative digital publishing is widening the breadth of scholarly impact beyond the conventional bibliometric community. Thus, research is becoming more reachable both inside and outside of academic institutions and are found to be shared, downloaded and discussed in social media. In this study, we linked the scienti!c articles found in mainstream news, weblogs and Stack Over"ow to the citation database of peer-reviewed literature called Scopus. We then explored how standard graph-based in"uence metrics can be used to measure the social impact of scienti!c articles. We also proposed the variant of Katz centrality metrics called EgoMet score to measure the local importance of scienti!c articles in its ego network. Later we evaluated these computed graph-based in"uence metrics by predicting absolute citations. Our results of the prediction model describe 34% variance to predict citations from blogs and mainstream news and 44% variance to predict citations from Stack Over"ow.