The way in which people acquire information on 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 becomes an opportunity to share points of view and information within the micro-blogging platforms such as Twitter, mainly aimed at satisfying their communication needs. Furthermore, the need to deepen the aspects related to the news stimulates a demand for information that is often met through online information sources, such as Wikipedia. This behaviour has also influenced the way in which journalists write their articles, requiring a careful assessment of what actually interests readers. The goal of this article is to dene a methodology for a recommender system able to suggest to the journalist, for a given event, the aspects still uncovered in news articles in which the readers' interest focuses. The basic idea is to characterize an event according to the echo it had 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 identies as recommendations the concepts that emerge as topic of interest from Twitter and Wikipedia, not covered in the published news articles.
Capturing Users' Information and Communication Needs for the Press Officers
MORBIDONI, Christian;
2017-01-01
Abstract
The way in which people acquire information on 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 becomes an opportunity to share points of view and information within the micro-blogging platforms such as Twitter, mainly aimed at satisfying their communication needs. Furthermore, the need to deepen the aspects related to the news stimulates a demand for information that is often met through online information sources, such as Wikipedia. This behaviour has also influenced the way in which journalists write their articles, requiring a careful assessment of what actually interests readers. The goal of this article is to dene a methodology for a recommender system able to suggest to the journalist, for a given event, the aspects still uncovered in news articles in which the readers' interest focuses. The basic idea is to characterize an event according to the echo it had 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 identies as recommendations the concepts that emerge as topic of interest from Twitter and Wikipedia, not covered in the published news articles.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
