Predicting citations from mainstream news, weblogs and discussion forums

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 scientific articles found in mainstream news, weblogs and Stack Overflow to the citation database of peer-reviewed literature called Scopus. We then explored how standard graph-based influence metrics can be used to measure the social impact of scientific articles. We also proposed the variant of Katz centrality metrics called EgoMet score to measure the local importance of scientific articles in its ego network. Later we evaluated these computed graph-based influence 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 Overflow.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 8 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.