Speak up, Fight Back! Detection of Social Media Disclosures of Sexual Harassment

Arijit Ghosh Chowdhury, Ramit Sawhney, Puneet Mathur, Debanjan Mahata, Rajiv Ratn Shah


Abstract
The #MeToo movement is an ongoing prevalent phenomenon on social media aiming to demonstrate the frequency and widespread of sexual harassment by providing a platform to speak narrate personal experiences of such harassment. The aggregation and analysis of such disclosures pave the way to development of technology-based prevention of sexual harassment. We contend that the lack of specificity in generic sentence classification models may not be the best way to tackle text subtleties that intrinsically prevail in a classification task as complex as identifying disclosures of sexual harassment. We propose the Disclosure Language Model, a three part ULMFiT architecture, consisting of a Language model, a Medium-Specific (Twitter) model and a Task-Specific classifier to tackle this problem and create a manually annotated real-world dataset to test our technique on this, to show that using a Discourse Language Model often yields better classification performance over (i) Generic deep learning based sentence classification models (ii) existing models that rely on handcrafted stylistic features. An extensive comparison with state-of-the-art generic and specific models along with a detailed error analysis presents the case for our proposed methodology.
Anthology ID:
N19-3018
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
136–146
Language:
URL:
https://www.aclweb.org/anthology/N19-3018
DOI:
10.18653/v1/N19-3018
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PDF:
http://aclanthology.lst.uni-saarland.de/N19-3018.pdf
Video:
 https://vimeo.com/359711283