University of Oulu

Kostakos, P. (2018) Public Perceptions on Organised Crime, Mafia, and Terrorism : A Big Data Analysis based on Twitter and Google Trends. International Journal of Cyber Criminology, 12(1), pp. 282-299. doi: 10.5281/zenodo.1467919

Public perceptions on organised crime, Mafia, and terrorism : a big data analysis based on Twitter and Google trends

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Author: Kostakos, Panos1
Organizations: 1Center for Ubiquitous Computing, Faculty of Information Technology and Electrical Engineering, University of Oulu
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.5 MB)
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Language: English
Published: K. Jaishankar, 2018
Publish Date: 2018-12-21


Public perceptions enable crime and motivate government policy on law and order; however, there has been limited empirical research on serious crime perceptions in social media. Recently, open source data—and ‘big data’—have enabled researchers from different fields to develop cost-effective methods for opinion mining and sentiment analysis. Against this backdrop, the aim of this paper is to apply state-of-the-art tools and techniques for assembly and analysis of open source data. We set out to explore how non-discursive behavioural data can be used as a proxy for studying public perceptions of serious crime. The data collection focused on the following three conversational topics: organised crime, the mafia, and terrorism. Specifically, time series data of users’ online search habits (over a ten-year period) were gathered from Google Trends, and cross-sectional network data (N=178,513) were collected from Twitter. The collected data contained a significant amount of structure. Marked similarities and differences in people’s habits and perceptions were observable, and these were recorded. The results indicated that ‘big data’ is a cost-effective method for exploring theoretical and empirical issues vis-à-vis public perceptions of serious crime.

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Series: International journal of cyber criminology
ISSN: 0974-2891
ISSN-E: 0974-2891
ISSN-L: 0974-2891
Volume: 12
Issue: 1
Pages: 282 - 299
DOI: 10.5281/zenodo.1467919
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: This work is (partially) funded by the European Commission grant 770469-CUTLER and 645706-GRAGE.
EU Grant Number: (770469) CUTLER - Coastal Urban developmenT through the LEnses of Resiliency
(645706) GRAGE - Grey and green in Europe: elderly living in urban areas
Copyright information: © 2018 International Journal of Cyber Criminology (Diamond Open Access Journal). Under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.