The following paper was presented at the Twelfth International AAAI Conference on Web and Social Media that took place last month at Stanford University.
Title
Predicting News Coverage of Scientific Articles
Authors
Ansel MacLaughlin
Northeastern University
John Wihbey
Northeastern University
David A. Smith
Northeastern University
Source
Proceedings of the Twelfth International AAAI Conference on Web and Social Media (ICWSM 2018)
Abstract
Journalists act as gatekeepers to the scientific world, control- ling what information reaches the public eye and how it is presented. Analyzing the kinds of research that typically re-ceive more media attention is vital to understanding issues such as the “science of science communication” (National Academies of Sciences, Engineering, and Medicine 2017), patterns of misinformation, and the “cycle of hype.” We track the coverage of 91,997 scientific articles published in 2016 across various disciplines, publishers, and news outlets us- ing metadata and text data from a leading tracker of sci- entific coverage in social and traditional media, Altmetric. We approach the problem as one of ranking each day’s, or week’s, papers by their likely level of media attention, us- ing the learning-to-rank model lambdaMART (Burges 2010). We find that ngram features from the title, abstract and press release significantly improve performance over the metadata features journal, publisher, and subjects.
Direct to Full Text Article
10 pages; PDF.
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