Public perceptions on organised crime, mafia, and terrorism : a big data analysis based on Twitter and Google trends
Kostakos, Panos (2018-10-21)
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
© 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.
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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