University of Oulu

P. Kostakos, L. Špráchalová, A. Pandya, M. Aboeleinen and M. Oussalah, "Covert Online Ethnography and Machine Learning for Detecting Individuals at Risk of Being Drawn into Online Sex Work," 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Barcelona, 2018, pp. 1096-1099. doi: 10.1109/ASONAM.2018.8508276

Covert online ethnography and machine learning for detecting individuals at risk of being drawn into online sex work

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Author: Kostakos, Panos1; Špráchalová, Lucie2; Pandya, Abhinay1;
Organizations: 1Center for Ubiquitous Computing, University of Oulu, Oulu, Finland
2Department of Sociology, Charles University, Prague, Prague, Czech Republic
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.8 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe201901222703
Language: English
Published: Institute of Electrical and Electronics Engineers, 2018
Publish Date: 2019-01-22
Description:

Abstract

How can we identify individuals at risk of being drawn into online sex work? The spread of online communication removes transaction costs and enables a greater number of people to be involved in illicit activities, including online sex trade. As a result, social media platforms often work as springboard for criminal careers posing a significant risk to the economy, public health and trust. Detecting deviant behaviors online is limited by the poor availability of ground-truth data and machine learning tools. Unlike prior work which focuses exclusively on either qualitative or quantitative methods, in this paper we combine covert online ethnography with semi-supervised learning methodologies, using data from a popular European adult forum. We obtained risk assessment results of 78 users using covert online ethnography, and set out to build a machine learning model that can predict the risk factor in other 28,832 users. Results show that a combination-based approach in which all features are used yields the most accurate results.

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Series: Proceedings of the International Conference on Advances in Social Network Analysis and Mining
ISSN: 2473-991X
ISSN-L: 2473-991X
ISBN: 978-1-5386-6052-2
ISBN Print: 978-1-5386-6051-5
Pages: 1096 - 1099
DOI: 10.1109/ASONAM.2018.8508276
OADOI: https://oadoi.org/10.1109/ASONAM.2018.8508276
Host publication: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
Conference: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
Subjects:
Funding: 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
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