Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2016

Abstract

Social media content, from platforms such as Twitter and Foursquare, has enabled an exciting new field of social sensing, where participatory content generated by users has been used to identify unexpected emerging or trending events. In contrast to such text-based channels, we focus on image-sharing social applications (specifically Instagram), and investigate how such urban social sensing can leverage upon the additional multi-modal, multimedia content. Given the significantly higher fraction of geotagged content on Instagram, we aim to use such channels to go beyond identification of long-lived events (e.g., a marathon) to achieve finer-grained characterization of multiple micro-events (e.g., a person winning the marathon) that occur over the lifetime of the macro-event. Via empirical analysis from a corpus of Instagram data from 3 international marathons, we establish the need for novel data pre-processing as: (a) semantic annotation of image content indeed provides additional features distinct from text captions, and (b) an appreciable fraction of the posted images do not pertain to the event under consideration. We propose a framework, called EiM, that combines such preprocessing with clustering-based event detection. We show that our initial prototype of EiM shows promising results: it is able to identify many micro-events in the three marathons, with spatial and temporal resolution that is less than 1% and 10%, respectively, of the corresponding ranges for the macro-event.

Keywords

Semantics, Twitter, Feature extraction, Spatiotemporal phenomena, Media, Sensors, Event detection, Information fusion, Semantics, Social networking (online)

Discipline

Databases and Information Systems | Social Media

Research Areas

Data Science and Engineering

Publication

2016 19th International Conference on Information Fusion (FUSION): Heidelberg, Germany, July 5-8

First Page

130

Last Page

137

ISBN

9780996452748

Publisher

IEEE

City or Country

Piscataway, NJ

Additional URL

https://ieeexplore.ieee.org/document/7527880/

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