An NLP Analysis of Exaggerated Claims in Science News

Yingya Li, Jieke Zhang, Bei Yu


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.
Anthology ID:
W17-4219
Volume:
Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
106–111
Language:
URL:
https://aclanthology.org/W17-4219
DOI:
10.18653/v1/W17-4219
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/W17-4219.pdf