A topic recommender for journalists View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-06-14

AUTHORS

Alessandro Cucchiarelli, Christian Morbidoni, Giovanni Stilo, Paola Velardi

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. More... »

PAGES

4-31

References to SciGraph publications

  • 2016-03-10. Leveraging Semantic Annotations to Link Wikipedia and News Archives in ADVANCES IN INFORMATION RETRIEVAL
  • 2008. Metrics for Evaluating the Serendipity of Recommendation Lists in NEW FRONTIERS IN ARTIFICIAL INTELLIGENCE
  • 2015-05-21. Efficient temporal mining of micro-blog texts and its application to event discovery in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2015. Journalism and Memorialization in the Age of Social Media in NONE
  • 2013. Entity Recommendations in Web Search in THE SEMANTIC WEB – ISWC 2013
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10791-018-9333-2

    DOI

    http://dx.doi.org/10.1007/s10791-018-9333-2

    DIMENSIONS

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