Skip to content

Latest commit

 

History

History
48 lines (33 loc) · 2.22 KB

README.md

File metadata and controls

48 lines (33 loc) · 2.22 KB
Raw

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.

https://ieeexplore.ieee.org/abstract/document/9458868

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.