Abstract
In this paper we present a recommender system, What To Write and Why (W3), 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 and Wikipedia, either not covered or poorly covered in the published news articles.
Cite
CITATION STYLE
Stilo, G., Velardi, P., Cucchiarelli, A., Marangoni, G., & Morbidoni, C. (2017). What to write? a topic recommender for journalists. In EMNLP 2017 - 2nd Workshop on Natural Language Processing Meets Journalism, NLPmJ 2017 - Proceedings of the Workshop (pp. 19–24). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w17-4204
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