# Extracting Incidents, Effects, and Requested Advice from MeToo Posts

arXiv: https://arxiv.org/abs/2303.10573 · HTML: https://mlsanity.com/paper/2303.10573/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 47 references that are in the map.
- First posted on arXiv 19 Mar 2023.

## Builds on (most cited first, 13 of 13)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [Crowdsourcing a Word-Emotion Association Lexicon](https://mlsanity.com/paper/1308.6297/) (2013)
- [Neural Summarization by Extracting Sentences and Words](https://mlsanity.com/paper/1603.07252/) (2016)
- [#MeTooMA: Multi-Aspect Annotations of Tweets Related to the MeToo Movement](https://mlsanity.com/paper/1912.06927/) (2019)
- [Contextual Affective Analysis: A Case Study of People Portrayals in Online #MeToo Stories](https://mlsanity.com/paper/1904.04164/) (2019)
- [Towards Automated Sexual Violence Report Tracking](https://mlsanity.com/paper/1911.06961/) (2019)
- [SafeCity: Understanding Diverse Forms of Sexual Harassment Personal Stories](https://mlsanity.com/paper/1809.04739/) (2018)
- [#MeTooMaastricht: Building a chatbot to assist survivors of sexual harassment](https://mlsanity.com/paper/1909.02809/) (2019)

## Abstract (arXiv)

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.