@inproceedings{bhat2021say,
author = {Bhat, Meghana Moorthy and Hosseini, Saghar and Awadallah, Ahmed and Bennett, Paul and Li, Weisheng},
title = {Say `YES' to Positivity: Detecting Toxic Language in Workplace Communications},
booktitle = {EMNLP 2021},
year = {2021},
month = {November},
abstract = {Workplace communication (e.g. email, chat, etc.) is a central part of enterprise productivity. Healthy conversations are crucial for creating an inclusive environment and maintaining harmony in an organization. Toxic communications at workplace can negatively impact over-all job satisfaction and are often subtle, hid-den or demonstrate human biases. The linguistic subtlety of mild yet hurtful conversations has made it difficult for researchers to quantify and extract toxic conversations automatically. While offensive language or hate speech has been extensively studied in social communities, there has been little work studying toxic workplace communications. Specifically, the lack of corpus, sparsity of toxicity in enterprise emails and a well-defined criteria for an-notating toxic conversations have prevented re-searchers from addressing the problem at scale. We take the first step towards studying toxicity in workplace communications by providing(1) a general and computationally viable taxonomy to study toxic language at workplace(2) a dataset to study toxic language at work-place based on the taxonomy and (3) analysis on why offensive language and hate-speech datasets are not suitable to detect workplace toxicity. Our implementation, analysis and data will be available at https://aka.ms/ToxiScope.},
url = {http://approjects.co.za/?big=en-us/research/publication/say-yes-to-positivity-detecting-toxic-language-in-workplace-communications/},
}