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ISPRS Int. J. Geo-Inf. 2017, 6(4), 120; doi:10.3390/ijgi6040120

Identifying Witness Accounts from Social Media Using Imagery

Department of Infrastructure Engineering, University of Melbourne, VIC 3010, Australia
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Academic Editors: Marinos Kavouras and Wolfgang Kainz
Received: 21 February 2017 / Revised: 31 March 2017 / Accepted: 10 April 2017 / Published: 18 April 2017
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Abstract

This research investigates the use of image category classification to distinguish images posted to social media that are Witness Accounts of an event. Only images depicting observations of the event, captured by micro-bloggers at the event, are considered Witness Accounts. Identifying Witness Accounts from social media is important for services such as news, marketing and emergency response. Automated image category classification is essential due to the large number of images on social media and interest in identifying witnesses in near real time. This paper begins research of this emerging problem with an established procedure, using a bag-of-words method to create a vocabulary of visual words and classifier trained to categorize the encoded images. In order to test the procedure, a set of images were collected for case study events, Australian Football League matches, from Twitter. Evaluation shows an overall accuracy of 90% and precision and recall for both classes exceeding 83%. View Full-Text
Keywords: image category classification; crowdsourcing; social media; transfer learning; visual bag-of-words image category classification; crowdsourcing; social media; transfer learning; visual bag-of-words
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Truelove, M.; Khoshelham, K.; McLean, S.; Winter, S.; Vasardani, M. Identifying Witness Accounts from Social Media Using Imagery. ISPRS Int. J. Geo-Inf. 2017, 6, 120.

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