Incongruent Headlines: Yet Another Way to Mislead Your Readers

Sophie Chesney, Maria Liakata, Massimo Poesio, Matthew Purver


Abstract
This paper discusses the problem of incongruent headlines: those which do not accurately represent the information contained in the article with which they occur. We emphasise that this phenomenon should be considered separately from recognised problematic headline types such as clickbait and sensationalism, arguing that existing natural language processing (NLP) methods applied to these related concepts are not appropriate for the automatic detection of headline incongruence, as an analysis beyond stylistic traits is necessary. We therefore suggest a number of alternative methodologies that may be appropriate to the task at hand as a foundation for future work in this area. In addition, we provide an analysis of existing data sets which are related to this work, and motivate the need for a novel data set in this domain.
Anthology ID:
W17-4210
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:
56–61
Language:
URL:
https://www.aclweb.org/anthology/W17-4210
DOI:
10.18653/v1/W17-4210
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-4210.pdf