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The Retrieval of Important News Stories by Influence Propagation among Communities and Categories

机译：通过社区和类别之间的影响力传播检索重要新闻报道

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摘要

Nowadays, people receive information of the news stories not only from newspapers but also from online news websites. They search important news stories in order to know what happen today. However, it is hard to browse all the news stories published on a day. It is necessary to identify which news stories are more newsworthy on the specific day. In this paper, we investigate how to automatically identify the importance of news stories for different news categories on a specific day by utilizing the influence propagation among communities and news categories. In particular, we build an influence propagation model which consists of three features: category relevance, bloggers' attention and bursty influence. Based on this influence propagation model, we propose a Cross-Category Social Influence Propagation (C-SIP) approach for scoring the importance of news stories on a specific day. We evaluate our approach by using the judgment of Story Ranking Task in TREC 2010 Blog Track. The experiment shows our approach attains a prominent performance in the retrieval of important news stories and gets 9.94% improvement over the best performance of participating systems in TREC 2010 Blog Track.
机译：如今，人们不仅从报纸而且从在线新闻网站接收新闻报道的信息。他们搜索重要的新闻报导，以了解今天发生的事情。但是，很难浏览一天中发布的所有新闻报道。有必要确定在特定日期哪些新闻报道更具新闻价值。在本文中，我们研究了如何通过利用社区和新闻类别之间的影响力传播来自动识别特定一天中不同新闻类别的新闻报道的重要性。特别是，我们建立了一个影响力传播模型，该模型包含三个特征：类别相关性，博客作者的关注度和突发性影响力。基于这种影响力传播模型，我们提出了一种跨类别的社会影响力传播（C-SIP）方法，用于评估特定日期新闻报道的重要性。我们通过使用TREC 2010 Blog Track中的故事排名任务的判断来评估我们的方法。实验表明，在TREC 2010 Blog Track中，我们的方法在重要新闻报道的检索中取得了突出的性能，并且比参与系统的最佳性能提高了9.94％。

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