What to Write? A topic recommender for journalists

Alessandro Cucchiarelli, Christian Morbidoni, Giovanni Stilo, Paola Velardi


Abstract
In this paper we present a recommender system, What To Write and Why, capable of suggesting to a journalist, for a given event, the aspects still uncovered in news articles on which the readers focus their interest. The basic idea is to characterize an event according to the echo it receives in online news sources and associate it with the corresponding readers’ communicative and informative patterns, detected through the analysis of Twitter and Wikipedia, respectively. Our methodology temporally aligns the results of this analysis and recommends the concepts that emerge as topics of interest from Twitter andWikipedia, either not covered or poorly covered in the published news articles.
Anthology ID:
W17-4204
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:
19–24
Language:
URL:
https://www.aclweb.org/anthology/W17-4204
DOI:
10.18653/v1/W17-4204
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-4204.pdf