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Evidential estimation of event locations in microblogs using the Dempster-Shafer theory

机译：使用Dempster-Shafer理论对微博中事件位置的证据估计

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摘要

Detecting real-world events by following posts in microblogs has been the motivation of numerous recent studies. In this work, we focus on the spatio-temporal characteristics of events detected in microblogs, and propose a method to estimate their locations using the Dempster-Shafer theory. We utilize three basic location-related features of the posts, namely the latitude-longitude metadata provided by the GPS sensor of the user's device, the textual content of the post, and the location attribute in the user profile, as three independent sources of evidence. Considering this evidence in a complementary way, we apply combination rules in the Dempster-Shafer theory to fuse them into a single model, and estimate the whereabouts of a detected event. Locations are treated at two levels of granularity, namely, city and town. Using the Dempster-Shafer theory to solve this problem allows uncertainty and missing data to be tolerated, and estimations to be made for sets of locations in terms of upper and lower probabilities. We demonstrate our solution using public tweets on Twitter posted in Turkey. The experimental evaluations conducted on a wide range of events including earthquakes, sports, weather, and street protests indicate higher success rates than the existing state of the art methods.
机译：通过跟踪微博中的帖子来检测现实世界的事件是许多近期研究的动机。在这项工作中，我们关注微博中检测到的事件的时空特征，并提出一种使用Dempster-Shafer理论估计事件位置的方法。我们利用帖子的三个基本的与位置相关的功能，即用户设备的GPS传感器提供的纬度-经度元数据，帖子的文本内容以及用户个人资料中的位置属性，作为三个独立的证据来源。考虑到这些证据的互补性，我们在Dempster-Shafer理论中应用组合规则将其融合为一个模型，并估计检测到的事件的下落。位置分为两个粒度级别，即城市和城镇。使用Dempster-Shafer理论来解决此问题，可以容忍不确定性和缺失数据，并可以根据上下概率对位置集进行估算。我们在土耳其发布的Twitter上使用公开推文演示了我们的解决方案。对包括地震，体育，天气和街头抗议在内的各种事件进行的实验评估表明，成功率要高于现有的最新方法。

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