BreakFast: Analyzing Celerity of News

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI:10.1109/ICMLA.2015.25

Shuguang Wang, Eui-Hong Han

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引用次数: 1

Abstract

In the hypercompetitive news market, news outlets race to break news first. In order to provide better breaking news service and improve the reader experience, news agencies need to understand how to identify bottlenecks and streamline their reporting and delivery processes. With that in mind, we built a system, BreakFast, to measure and compare the speed of delivery of breaking news from various news sources to readers. One of the primary challenges of this comparison is how to identify which breaking news items are about the same emerging event but reported by different news agencies with different headlines and content. To tackle this problem, we extracted keywords automatically from the content, identified important topics, and then developed a classification model. The model identifies the same breaking stories from multiple news sources with an accuracy of approximately 90%. We also proposed new metrics to evaluate the speed of breaking news services and built real-time dashboards to monitor performance over time. We deployed BreakFast into the breaking news service at The Washington Post. This integrated system narrowed in on bottlenecks in its breaking news generation and delivery process, and improved its breaking news service in terms of time by more than 50%.

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早餐:分析新闻的快慢

在竞争激烈的新闻市场上，新闻媒体竞相抢先发布新闻。为了提供更好的突发新闻服务，改善读者体验，新闻机构需要了解如何识别瓶颈，并简化他们的报道和交付流程。考虑到这一点，我们建立了一个系统，早餐，衡量和比较从各种新闻来源向读者传递突发新闻的速度。这种比较的主要挑战之一是如何识别哪些突发新闻是关于相同的新兴事件，但由不同的新闻机构以不同的标题和内容报道。为了解决这个问题，我们从内容中自动提取关键字，识别重要主题，然后开发分类模型。该模型从多个新闻来源中识别相同的突发事件，准确率约为90%。我们还提出了新的指标来评估突发新闻服务的速度，并建立了实时仪表板来监控性能。我们将《早餐》部署到《华盛顿邮报》的突发新闻服务中。这一集成系统解决了突发新闻生成和发布过程中的瓶颈，将突发新闻服务的时间提高了50%以上。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)

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