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A topic recommender for journalists

  • Alessandro Cucchiarelli
  • Christian Morbidoni
  • Giovanni Stilo
  • Paola Velardi
Social Media for Personalization and Search

Abstract

The way in which people gather information about events and form their own opinion on them has changed dramatically with the advent of social media. For many readers, the news gathered from online sources has become an opportunity to share points of view and information within micro-blogging platforms such as Twitter, mainly aimed at satisfying their communication needs. Furthermore, the need to deepen the aspects related to news stimulates a demand for additional information which is often met through online encyclopedias, such as Wikipedia. This behaviour has also influenced the way in which journalists write their articles, requiring a careful assessment of what actually interests the readers. The goal of this paper is to 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 and Wikipedia, either not covered or poorly covered in the published news articles.

Keywords

Recommender systems Wikipedia Twitter Online News Event detection Temporal mining 

Notes

Acknowledgements

This work has been partially supported by the MIUR under grant “Dipartimenti di eccellenza 2018–2022” of the Department of Computer Science of Sapienza University and by the IBM Faculty Award #2305895190.

Finally, we would like to thank SpazioDati (http://spaziodati.eu) and Textrazor(http://textrazor.com) for supporting this research by granting extensive access to their APIs.

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Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Università Politecnica delle MarcheAnconaItaly
  2. 2.Sapienza University of RomeRomeItaly

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