Along the Margins: Marginalized Communities' Ethical Concerns about Social Platforms

TL;DR

This study examines marginalized communities' ethical concerns on social platforms, highlighting issues like discrimination and misrepresentation, and evaluates NLP methods for automatic classification of these concerns to improve platform accountability.

Contribution

It provides a novel dataset of ethical concerns from marginalized communities' social media mentions and assesses NLP techniques for automatic detection of these concerns.

Findings

Ethical concerns mainly involve discrimination and misrepresentation.

Current software practices have significant deficiencies affecting marginalized groups.

NLP can effectively classify ethical concerns from social media data.

Abstract

In this paper, we identified marginalized communities' ethical concerns about social platforms. We performed this identification because recent platform malfeasance indicates that software teams prioritize shareholder concerns over user concerns. Additionally, these platform shortcomings often have devastating effects on marginalized populations. We first scraped 586 marginalized communities' subreddits, aggregated a dataset of their social platform mentions and manually annotated mentions of ethical concerns in these data. We subsequently analyzed trends in the manually annotated data and tested the extent to which ethical concerns can be automatically classified by means of natural language processing (NLP). We found that marginalized communities' ethical concerns predominantly revolve around discrimination and misrepresentation, and reveal deficiencies in current software development practices. As such, researchers and developers could use our work to further investigate these concerns and rectify current software flaws.