Research Article
Capturing Racial & Gender Inequities on Social Media Platforms using Machine Learning
@ARTICLE{10.4108/eetct.v9i31.1879, author={Sonika Malik and Harshita Chopra and Aniket Vashishtha}, title={Capturing Racial \& Gender Inequities on Social Media Platforms using Machine Learning}, journal={EAI Endorsed Transactions on Creative Technologies}, volume={9}, number={31}, publisher={EAI}, journal_a={CT}, year={2022}, month={7}, keywords={Social Media Analytics, Aspect Extraction, Machine Learning, Natural Language Processing}, doi={10.4108/eetct.v9i31.1879} }- Sonika Malik
Harshita Chopra
Aniket Vashishtha
Year: 2022
Capturing Racial & Gender Inequities on Social Media Platforms using Machine Learning
CT
EAI
DOI: 10.4108/eetct.v9i31.1879
Abstract
Online social media platforms provide a continuously evolving database due to the highly increasing popularity and rapid expansion of its user base. Users share their life experiences towards various inequity incidents faced at the workplace on the basis of their race or gender on these platforms while maintaining their anonymity. We aim at utilising famous social media platforms to perform extensive analysis and classification tasks for posts capturing instances of various types of Inequalities prevalent in today’s workplace. We present a framework to mine opinions expressed towards sexual harassment, mental health, racial injustice and gender-based bias in the corporate workplace using NLP techniques on social media data. The documents are represented by semantic similarity to aspect embedding’s captured using an attention-based framework for aspect extraction. In addition, we used scores from Empath categories to add information related to emotional facets.
Copyright © 2022 Sonika Malik et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
