Semi-supervised classification of social media posts : identifying sex-industry posts to enable better support for those experiencing sex-trafficking

Social media is both helpful and harmful to the work against sex trafficking. On one hand, social workers carefully use social media to support individuals experiencing sex trafficking. On the other hand, traffickers use social media to groom and recruit individuals into trafficking situations. Additionally, individuals experiencing sex trafficking can use social media as a means to meet sales quotas set by the traffickers [1]. There is the opportunity to use social media data to better provide support for people experiencing trafficking. While Artificial Intelligence and Machine Learning 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 individuals of all ages experiencing trafficking.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• Simonson, Ellie Louise.

Advisor dc:contributor.advisor

• Richard Fletcher.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/130709

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/130709