Linguistic Benchmarks of Online News Article Quality

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.

Filipa Peleja

Filipa Peleja is a data scientist in the Big Data Analytics team at Vodafone. She holds a Ph.D. in Computer Science having studied topics in machine learning, information retrieval, natural language processing, sentiment analysis and recommendation systems. During her Ph.D. she had the opportunity to enroll in a nine month internship at Yahoo! Labs and work as a data scientist researcher at Eurecat Technology Centre of Catalonia. Filipa Peleja has been involved in several computer software projects in collaboration with private companies, public institutions and academia. She studied at the Faculty of Science and Technology in the NOVA University of Lisbon obtaining Computer Software/Informatics Engineering (M.Sc., B.Sc.). Also, worked as a Business Consultant at IP2CS and as a researcher at research centers CITI and NOVALINCS. Filipa Peleja has published several scientific articles and demos in international conferences.Vodafone