Using Sensor Metadata Streams to Identify Topics of Local Events in the City

Albakour, M.-D., Macdonald, C. and Ounis, I. (2015) Using Sensor Metadata Streams to Identify Topics of Local Events in the City. In: 38th Annual ACM SIGIR Conference (SIGIR 2015), Santiago, Chile, 9-13 Aug 2015, pp. 711-714. ISBN 9781450336215 (doi: 10.1145/2766462.2767837)

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In this paper, we study the emerging Information Retrieval (IR) task of local event retrieval using sensor metadata streams. Sensor metadata streams include information such as the crowd density from video processing, audio classifications, and social media activity. We propose to use these metadata streams to identify the topics of local events within a city, where each event topic corresponds to a set of terms representing a type of events such as a concert or a protest. We develop a supervised approach that is capable of mapping sensor metadata observations to an event topic. In addition to using a variety of sensor metadata observations about the current status of the environment as learning features, our approach incorporates additional background features to model cyclic event patterns. Through experimentation with data collected from two locations in a major Spanish city, we show that our approach markedly outperforms an alternative baseline. We also show that modelling background information improves event topic identification.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Macdonald, Professor Craig and Ounis, Professor Iadh
Authors: Albakour, M.-D., Macdonald, C., and Ounis, I.
College/School:College of Science and Engineering > School of Computing Science
Copyright Holders:Copyright © 2015 ACM
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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