EURO 2024 Copenhagen
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3827. Identifying high-risk job advertisements on social media to help mitigate labour exploitation.

Invited abstract in session WA-6: Advancements of OR-analytics in statistics, machine learning and data science 16, stream Advancements of OR-analytics in statistics, machine learning and data science.

Wednesday, 8:30-10:00
Room: 1013 (building: 202)

Authors (first author is the speaker)

1. Preeti Sharma
Leeds Institute for Data Analytics, University of Leeds
2. Mahnaz Hosseinzadeh
3. Sajid Siraj
Leeds University Business School, University of Leeds

Abstract

Millions of individuals are subjected to exploitative labour practices worldwide. This project focusses on intervention at the recruitment stage, where deceptive job advertisements often proliferate unchecked. We propose a machine learning approach to identify high-risk online job adverts potentially leading to labour exploitation. With the help of natural language processing techniques such as word embeddings, we extract relevant features from a sample of job adverts, obtained in collaboration with a non-profit organisation. Using these features, we then train a predictive model to distinguish high-risk adverts from others. We validate our model using a different set of job adverts which are unseen to the model. Through this work we demonstrate the use of machine learning to help vulnerable job seekers by facilitating timely intervention. This work represents a crucial step towards mitigating the risk of labour exploitation in the digital age and safeguarding the rights of workers.

Keywords

Status: accepted

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