InfoShield: Generalizable Information-Theoretic Human-Trafficking Detection
Lee, M.C., Vajiac, C., Kulshrestha, A., Levy, S., Park, N., Jones, C., Rabbany, R., and Faloutsos, C., "InfoShield: Generalizable Information-Theoretic Human-Trafficking Detection". 37th IEEE International Conference on Data Engineering (ICDE), 2021.
Please cite the paper as:
@inproceedings{lee2021InfoShield,
title={{InfoShield:} Generalizable Information-Theoretic Human-Trafficking Detection},
author={Lee, Meng-Chieh and Vajiac, Catalina and Kulshrestha, Aayushi and Levy, Sacha and Park, Namyong and Jones, Cara and Rabbany, Reihaneh and Faloutsos, Christos},
booktitle={2021 37th IEEE International Conference on Data Engineering (ICDE)},
year={2021},
organization={IEEE},
}
Introduction
In this paper, we present INFOSHIELD, which makes the following contributions:
- Practical: being scalable and effective on real data
- Parameter-free and Principled: requiring no user-defined parameters
- Interpretable: finding a document to be the cluster representative, highlighting all the common phrases, and automatically detecting “slots”, i.e. phrases that differ in every document
- Generalizable: beating or matching domainspecific methods in Twitter bot detection and human trafficking detection respectively, as well as being language-independent finding clusters in Spanish, Italian, and Japanese.
Usage
To run the InfoShield demo:
make demo
To run InfoShield on the given example:
python infoshield.py data/sample_input.csv id text
To specify the column headers for unique id (id_str) and text (text_str):
python infoshield.py CSV_FILENAME id_str text_str
To run InfoShield-Coarse only:
python infoshieldcoarse.py CSV_FILENAME
To run InfoShield-Fine only:
python infoshieldfine.py CSV_REUSLT_FROM_COARSE
Acknowledgement
One part of our code is based on Partial Order Alignment, downloaded from https://github.com/ljdursi/poapy.
This implementation is according to the following paper:
Lee, C., Grasso, C., & Sharlow, M. F. (2002). Multiple sequence alignment using partial order graphs. Bioinformatics, 18(3), 452-464.