Jieke Zhang


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An NLP Analysis of Exaggerated Claims in Science News
Yingya Li | Jieke Zhang | Bei Yu
Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism

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.