University of Oulu

P. Kostakos, S. Robroo, B. Lin and M. Oussalah, "Crime Prediction Using Hotel Reviews?," 2019 European Intelligence and Security Informatics Conference (EISIC), Oulu, Finland, 2019, pp. 134-137, doi: 10.1109/EISIC49498.2019.9108861

Crime prediction using hotel reviews?

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Author: Kostakos, Panos1; Robroo, Somkiadcharoen1; Lin, Bofan1;
Organizations: 1Center for Ubiquitous Computing, University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.5 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2020061744657
Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2020-06-17
Description:

Abstract

Can hotel reviews be used as a proxy for predicting crime hotspots? Domain knowledge indicates that hotels are crime attractors, and therefore, hotel guests might be reliable “human crime sensors”. In order to assess this heuristic, we propose a novel method by mapping actual crime events into hotel reviews from London, using spatial clustering and sentiment feedback. Preliminary findings indicate that sentiment scores from hotel reviews are inversely correlated with crime intensity. Hotels with positive reviews are more likely to be adjacent to crime hotspots, and vice versa. One possible explanation for this counterintuitive finding that the review data are not mapped against specific crime types, and thus the crime data capture mostly police visibility on the site. More research and domain knowledge are needed to establish the strength of hotel reviews as a proxy for crime prediction.

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ISBN: 978-1-7281-6735-0
ISBN Print: 978-1-7281-6736-7
Pages: 134 - 137
DOI: 10.1109/EISIC49498.2019.9108861
OADOI: https://oadoi.org/10.1109/EISIC49498.2019.9108861
Host publication: 2019 European Intelligence and Security Informatics Conference (EISIC)
Conference: European Intelligence and Security Informatics Conference
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Subjects:
Funding: This work is (partially) funded by the European Commission grant PRINCE (815362).
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