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Sense and Focus: Towards Effective Location Inference and Event Detection on Twitter

机译：感觉和焦点：对Twitter上有效的位置推断和事件检测

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摘要

Twitter users post observations about their immediate environment as a part of the 500 million tweets posted everyday. As such, Twitter can become the source for invaluable information about objects, locations, and events, which can be analyzed and monitored in real time, not only to understand what is happening in the world, but also an event's exact location. However, Twitter data is noisy as sensory values, and information such as the location of a tweet may not be available, e.g., only 0.9 % of tweets have GPS data. Due to the lack of accurate and fine-grained location information, existing Twitter event monitoring systems focus on city-level or coarser location identification, which cannot provide details for local events. In this paper, we propose SNAF (Sense and Focus), an event monitoring system for Twitter data that emphasizes local events. We increase the availability of the location information significantly by finding locations in tweet messages and users' past tweets. We apply data cleaning techniques in our system, and with extensive experiments, we show that our method can improve the accuracy of location inference by 5 % to 20 % across different error ranges. We also show that our prototype implementation of SNAF can identify critical local events in real time, in many cases earlier than news reports.
机译：Twitter用户在每天发布的5000万推文的一部分后，将观察结果发布。因此，Twitter可以成为可以实时分析和监控的对象，位置和事件的宝贵信息的源，不仅要了解世界发生的事情，还可以是事件的确切位置。但是，Twitter数据是嘈杂的，作为感官值，例如，诸如推文的位置的信息可能不可用，例如，仅有0.9％的推文具有GPS数据。由于缺乏准确和细粒度的位置信息，现有的Twitter事件监控系统专注于城市级或较粗糙的位置识别，这不能为当地事件提供详细信息。在本文中，我们提出了SNAF（感觉和焦点），了解了强调当地事件的推特数据的事件监控系统。我们通过在推文消息和用户过去推文中查找位置来提高位置信息的可用性。我们在我们的系统中应用数据清洁技术，并且通过广泛的实验，我们表明我们的方法可以将位置推理的准确性提高5％至20％在不同的误差范围内。我们还表明，我们的Prototype实施SNAF可以实时识别关键的本地事件，在许多情况下比新闻报道更早。

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