An event-based POI service from microblogs

• Published in: 2011 13th Asia-Pacific Network Operations and Management Symposium pp. 1 - 4
• Main Authors: Chun-Shuo Lin, Meng-Fen Chiang, Wen-Chih Peng, Chien-Cheng Chen
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2011
• Subjects: Clustering algorithms; Event detection; Feature extraction; Google; Semantics; Spatiotemporal phenomena; Web sites
• ISBN: 1457716682; 9781457716683
• DOI: 10.1109/APNOMS.2011.6076994

A point of interest (POI) is a location that users can find something interesting. The POI information is typically made by the POI service provider and users can also add comments to a POI or add a new POI to enrich the POI contents. However, the information does not update frequently and some new points or time-sensitive events may not be marked in the POI service. Therefore, we construct a framework to extract the events from microblog to support an event-based POI service different from traditional POI service. With this kind of application, users can easily to get the events around them. In Web 2.0, microblog has become an important media spreading information in the web. With the help of mobile devices, microblogs can spread the information with greater spatiotemporal sensitivity. We take the spatial and temporal features of messages in microblogs to detect spatiotemporal events and use it to enrich POI service. This work aims at detecting spatiotemporal events in microblogs with an pair {location, time} from an handset device and feedbacks information to the users. To achieve this goal, we propose a framework with 4 phases: (1) profile construction; (2) feature extraction; (3) event summary detection; (4) ranking events. In this paper, we propose STF(standing for Spatio-Temporal Focus) value to evaluate the distinctiveness of a feature. Furthermore, we combine STF value with document overlap to expand an event cluster from an event seed. To extract the top-k possible event summaries, an efficient top-k soft clustering algorithm is proposed in this paper. In the experiments, we use real data set from Twitter to verify our proposed framework.