Reuters Tracer: A Large Scale System of Detecting & Verifying Real-Time News Events from Twitter

Proceedings of the 25th ACM International on Conference on Information and Knowledge Management Pub Date : 2016-10-24 DOI:10.1145/2983323.2983363

Xiaomo Liu, Quanzhi Li, Armineh Nourbakhsh, Rui Fang, Merine Thomas, Kajsa Anderson, Russell Kociuba, Mark Vedder, Steven Pomerville, Ramdev Wudali, Robert Martin, John Duprey, Arun Vachher, William Keenan, Sameena Shah

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引用次数: 58

Abstract

News professionals are facing the challenge of discovering news from more diverse and unreliable information in the age of social media. More and more news events break on social media first and are picked up by news media subsequently. The recent Brussels attack is such an example. At Reuters, a global news agency, we have observed the necessity of providing a more effective tool that can help our journalists to quickly discover news on social media, verify them and then inform the public. In this paper, we describe Reuters Tracer, a system for sifting through all noise to detect news events on Twitter and assessing their veracity. We disclose the architecture of our system and discuss the various design strategies that facilitate the implementation of machine learning models for noise filtering and event detection. These techniques have been implemented at large scale and successfully discovered breaking news faster than traditional journalism

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Reuters Tracer:一个大规模的检测和验证Twitter实时新闻事件的系统

在社交媒体时代，新闻工作者正面临着从更加多样化和不可靠的信息中发现新闻的挑战。越来越多的新闻事件首先在社交媒体上爆发，随后被新闻媒体报道。最近的布鲁塞尔袭击就是这样一个例子。在全球新闻机构路透社，我们发现有必要提供一种更有效的工具，帮助我们的记者在社交媒体上快速发现新闻，进行核实，然后通知公众。在本文中，我们描述了Reuters Tracer，这是一个筛选Twitter上所有噪音以检测新闻事件并评估其真实性的系统。我们揭示了我们系统的架构，并讨论了促进实现用于噪声过滤和事件检测的机器学习模型的各种设计策略。这些技术已经大规模实施，并成功地比传统新闻更快地发现突发新闻

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Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

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