Semi-Supervised Classification of Social Media Posts: Identifying Sex-Industry Posts to Enable Better Support for Those Experiencing Sex-Trafficking

TL;DR

This paper explores semi-supervised machine learning methods to classify social media posts related to the sex industry, aiming to improve support for trafficking victims by leveraging limited labeled data and clustering techniques.

Contribution

It introduces a semi-supervised classification approach using FastText and Doc2Vec embeddings combined with clustering to identify sex industry posts on social media with high accuracy.

Findings

FastText CBOW achieved 98.6% accuracy on 12,000 posts

Semi-supervised learning can effectively label large social media datasets

Potential to develop tools for mapping sex industry activity on social media

Abstract

Social media is both helpful and harmful to the work against sex trafficking. On one hand, social workers carefully use social media to support people experiencing sex trafficking. On the other hand, traffickers use social media to groom and recruit people into trafficking situations. There is the opportunity to use social media data to better provide support for people experiencing trafficking. While AI and Machine Learning (ML) have been used in work against sex trafficking, they predominantly focus on detecting Child Sexual Abuse Material. Work using social media data has not been done with the intention to provide community level support to people of all ages experiencing trafficking. Within this context, this thesis explores the use of semi-supervised classification to identify social media posts that are a part of the sex industry. Several methods were explored for ML. However, the primary method used was semi-supervised learning, which has the benefit of providing automated classification with a limited set of labelled data. Social media posts were embedded into low-dimensional vectors using FastText and Doc2Vec models. The data were then clustered using k-means clustering, and cross-validation was used to determine label propagation accuracy. The results of the semi-supervised algorithm were encouraging. The FastText CBOW model provided 98.6% accuracy to over 12,000 posts in clusters where label propagation was applied. The results of this thesis suggest that further semi-supervised learning, in conjunction with manual labeling, may allow for the entire dataset containing over 50,000 posts to be accurately labeled. A fully labeled dataset could be used to develop a tool to identify an overview of where and when social media is used within the sex industry. This could be used to help determine better ways to provide support to people experiencing trafficking.