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.

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记者的主题推荐者

随着社交媒体的出现，人们收集有关事件的信息并对事件形成自己的看法的方式已经发生了巨大变化。对于许多读者而言，从在线来源收集的新闻已成为在微博平台（例如Twitter）中分享观点和信息的机会，主要目的是满足他们的交流需求。此外，加深与新闻有关的方面的需求激发了对附加信息的需求，而这些附加信息通常通过诸如Wikipedia之类的在线百科全书来满足。这种行为也影响了记者撰写文章的方式，需要仔细评估读者真正感兴趣的内容。本文的目的是提出一种推荐系统，即“写什么和为什么写”，它能够针对特定事件向记者提供建议，新闻报道中仍未发现的方面是读者关注的方面。基本思想是根据事件在在线新闻源中收到的回声来表征事件，并将其与相应的读者的交流和信息模式相关联，分别通过对Twitter和Wikipedia的分析进行检测。我们的方法在时间上可以使分析结果保持一致，并推荐Twitter和Wikipedia感兴趣的主题出现的概念，这些概念可能在已发表的新闻文章中未涉及或覆盖率不高。通过对Twitter和Wikipedia的分析分别检测到。我们的方法在时间上可以使分析结果保持一致，并推荐Twitter和Wikipedia感兴趣的主题出现的概念，这些概念可能在已发表的新闻文章中未涉及或覆盖率不高。通过对Twitter和Wikipedia的分析分别检测到。我们的方法在时间上可以使分析结果保持一致，并推荐Twitter和Wikipedia感兴趣的主题出现的概念，这些概念可能在已发表的新闻文章中未涉及或覆盖率不高。