Stress level detection via OSN usage pattern and chronicity analysis: An OSINT threat intelligence module

Abstract

Online Social Networks (OSN) are not only a popular communication and entertainment platform but also a means of self-representation. In this paper, we adopt an interdisciplinary approach combining Open Source Intelligence (OSINT) and user-generated content classification techniques with a user-driven stress test as applied to a Greek community of OSN users. The main goal of the paper is to study the chronicity of the stress level users experience, as depicted by OSN user generated content. In order to achieve that, we investigate whether collected data are able to facilitate the process of stress level detection. To this end, we perform unsupervised flat data classification of the user-generated content and formulate two working clusters which classify usage patterns that depict medium-to-low and medium-to-high stress levels respectively. To address the main goal of the paper, we divide user-generated content into chronologically defined sub-periods in order to study potential usage fluctuations over time. To this extent, we follow a process that includes (a) content classification into predefined categories of interest, (b) usage pattern metrics extraction and (c) metrics and clusters utilisation towards usage pattern fluctuation detection both through the prism of users’ usual usage pattern and its correlation to the depicted stress level. Such an approach enables detection of time periods when usage pattern deviates from the usual and correlates such deviations to user experienced stress level. Finally, we highlight and comment on the emerging ethical issues regarding the classification of OSN user-generated content.

DOI: 10.1016/j.cose.2016.12.003

13 Figures and Tables

Cite this paper

@article{Kandias2017StressLD, title={Stress level detection via OSN usage pattern and chronicity analysis: An OSINT threat intelligence module}, author={Miltiadis Kandias and Dimitris Gritzalis and Vasilis Stavrou and Kostas Nikoloulis}, journal={Computers & Security}, year={2017}, volume={69}, pages={3-17} }