Covert Online Ethnography and Machine Learning for Detecting Individuals at Risk of Being Drawn into Online Sex Work
@article{Kostakos2018CovertOE, title={Covert Online Ethnography and Machine Learning for Detecting Individuals at Risk of Being Drawn into Online Sex Work}, author={Panos Kostakos and Lucie Sprachalova and Abhinay Pandya and Mohamed Aboeleinen and Mourad Chabane Oussalah}, journal={2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)}, year={2018}, pages={1096-1099} }
How can we identify individuals at risk of being drawn into online sex work. [] Key Method 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…
One Citation
A Human-Centered Systematic Literature Review of the Computational Approaches for Online Sexual Risk Detection
- Computer ScienceProc. ACM Hum. Comput. Interact.
- 2021
A comprehensive literature review to analyze 73 peer-reviewed articles on computational approaches utilizing text or meta-data/multimedia for online sexual risk detection found that the majority of work has focused on identifying sexual predators after-the-fact, rather than taking more nuanced approaches to identify potential victims and problematic patterns that could be used to prevent victimization before it occurs.
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