Extracting Incidents, Effects, and Requested Advice from MeToo Posts

TL;DR

This paper presents a natural language processing model to extract incident, effect, and advice-seeking information from Reddit MeToo posts, aiding helpers in understanding survivors' needs efficiently.

Contribution

It introduces a novel sentence classification model for MeToo posts and provides a large labeled dataset, MeThree, for further research.

Findings

Model achieves macro F1 score of 0.82 on cross-validation.

MeThree dataset contains 8,947 labeled sentences.

LIWC-22 analysis reveals language differences across categories.

Abstract

Survivors of sexual harassment frequently share their experiences on social media, revealing their feelings and emotions and seeking advice. We observed that on Reddit, survivors regularly share long posts that describe a combination of (i) a sexual harassment incident, (ii) its effect on the survivor, including their feelings and emotions, and (iii) the advice being sought. We term such posts MeToo posts, even though they may not be so tagged and may appear in diverse subreddits. A prospective helper (such as a counselor or even a casual reader) must understand a survivor's needs from such posts. But long posts can be time-consuming to read and respond to. Accordingly, we address the problem of extracting key information from a long MeToo post. We develop a natural language-based model to identify sentences from a post that describe any of the above three categories. On ten-fold cross-validation of a dataset, our model achieves a macro F1 score of 0.82. In addition, we contribute MeThree, a dataset comprising 8,947 labeled sentences extracted from Reddit posts. We apply the LIWC-22 toolkit on MeThree to understand how different language patterns in sentences of the three categories can reveal differences in emotional tone, authenticity, and other aspects.