Paper Info
Title
What to write and why: a recommender for news media.
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 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 behavior 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.
Year
DOI
Venue
2018
10.1145/3167132.3167274
SAC 2018: Symposium on Applied Computing Pau France April, 2018
Keywords
Field
DocType
Recommender systems, Wikipedia, Twitter, social networks, on line news, event detection, temporal mining
Recommender system,World Wide Web,Social media,Social network,Computer science,News media,Encyclopedia
Conference
ISBN
Citations 
PageRank 
978-1-4503-5191-1
0
0.34
References 
Authors
24
4
Name
Order
Citations
PageRank
Alessandro Cucchiarelli122636.38
Christian Morbidoni228937.76
Giovanni Stilo311116.65
paola velardi41553163.66