Linguistic Benchmarks of Online News Article Quality

Ioannis Arapakis, Filipa Peleja, Barla Berkant Cambazoglu, João Magalhães

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)

Abstract

Online news editors ask themselves the same question many times: what is missing in this news article to go online? This is not an easy question to be answered by computational linguistic methods. In this work, we address this important question and characterise the constituents of news article editorial quality. More specifically, we identify 14 aspects related to the content of news articles. Through a correlation analysis, we quantify their independence and relation to assessing an article's editorial quality. We also demonstrate that the identified aspects, when combined together, can be used effectively in quality control methods for online news. © 2016 Association for Computational Linguistics.
Original languageEnglish
Title of host publicationProceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, August 7-12, 2016, Berlin, Germany, Volume 1: Long Papers
Pages1893-1902
Volume4
ISBN (Electronic)978-151082758-5
Publication statusPublished - 2016
Event54th Annual Meeting of the Association for Computational Linguistics - Berlin, Germany
Duration: 7 Aug 201612 Aug 2016

Conference

Conference54th Annual Meeting of the Association for Computational Linguistics
Abbreviated titleACL 2016
CountryGermany
CityBerlin
Period7/08/1612/08/16

Keywords

  • Benchmarking
  • Computational linguistics
  • Linguistics
  • Correlation analysis
  • Editorial quality
  • News articles

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