An nlp analysis of exaggerated claims in science news

Yingya Li, Jieke Zhang, Bei Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationEMNLP 2017 - 2nd Workshop on Natural Language Processing Meets Journalism, NLPmJ 2017 - Proceedings of the Workshop
EditorsOctavian Popescu, Carlo Strapparava
PublisherAssociation for Computational Linguistics (ACL)
Pages106-111
Number of pages6
ISBN (Electronic)9781945626883
StatePublished - 2017
Externally publishedYes
EventEMNLP 2017 2nd Workshop on Natural Language Processing Meets Journal., NLPmJ 2017 - Copenhagen, Denmark
Duration: Sep 7 2017 → …

Publication series

NameEMNLP 2017 - 2nd Workshop on Natural Language Processing Meets Journalism, NLPmJ 2017 - Proceedings of the Workshop

Conference

ConferenceEMNLP 2017 2nd Workshop on Natural Language Processing Meets Journal., NLPmJ 2017
Country/TerritoryDenmark
CityCopenhagen
Period9/7/17 → …

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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