Predicting citations from mainstream news, weblogs and discussion forum

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Date
2017-08-23Author
Timilsina, Mohan
Davis, Brian
Taylor, Mike
Hayes, Conor
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Cited 5 times in Scopus (view citations)
Recommended Citation
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
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Abstract
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.