@InProceedings{li-zhang-yu:2017:NLPmJ,
  author    = {LI, YINGYA  and  Zhang, Jieke  and  Yu, Bei},
  title     = {An NLP Analysis of Exaggerated Claims in Science News},
  booktitle = {Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {106--111},
  abstract  = {The discrepancy between science and media has been affecting the effectiveness
	of science communication. Original findings from science publications may be
	distorted with altered claim strength when reported to the public, causing
	misinformation spread. This study conducts an NLP analysis of exaggerated
	claims in science news, and then constructed prediction models for identifying
	claim strength levels in science reporting. The results demonstrate different
	writing styles journal articles and news/press releases use for reporting
	scientific findings. Preliminary prediction models reached promising result
	with room for further improvement.},
  url       = {http://www.aclweb.org/anthology/W17-4219}
}