论文标题
地年代社会位置分类：将类型与基于地理标记社交媒体帖子的地方关联
Geosocial Location Classification: Associating Type to Places Based on Geotagged Social-Media Posts
论文作者
论文摘要
将类型与位置相关联可以用来丰富地图，并且可以提供大量地理空间应用。一种自动的方法可以使该过程在人工劳动方面降低，并更快地对变化做出反应。在本文中，我们研究了地理社会位置分类的问题，其中基于社交媒体帖子发现了站点的类型，例如建筑物。我们的目标是正确地将一组在给定位置周围的小半径张贴的消息与相应的位置类型相关联，例如学校，教堂，餐厅或博物馆。我们探索了问题的两种方法：（a）首先对每个消息进行分类的管道方法，然后从各个消息标签中推断出与消息集关联的位置； （b）同时处理各个消息以产生所需位置类型的联合方法。我们在一个地理标签的数据集上测试了两种方法。我们的结果表明了联合方法的优越性。此外，我们表明，由于该问题的独特结构，在该结构中共同处理了与弱相关的消息，以产生单个最终标签，线性分类器的表现优于深度神经网络的替代方案。
Associating type to locations can be used to enrich maps and can serve a plethora of geospatial applications. An automatic method to do so could make the process less expensive in terms of human labor, and faster to react to changes. In this paper we study the problem of Geosocial Location Classification, where the type of a site, e.g., a building, is discovered based on social-media posts. Our goal is to correctly associate a set of messages posted in a small radius around a given location with the corresponding location type, e.g., school, church, restaurant or museum. We explore two approaches to the problem: (a) a pipeline approach, where each message is first classified, and then the location associated with the message set is inferred from the individual message labels; and (b) a joint approach where the individual messages are simultaneously processed to yield the desired location type. We tested the two approaches over a dataset of geotagged tweets. Our results demonstrate the superiority of the joint approach. Moreover, we show that due to the unique structure of the problem, where weakly-related messages are jointly processed to yield a single final label, linear classifiers outperform deep neural network alternatives.
