@inproceedings{davidson-etal-2020-developing,
title = "Developing a New Classifier for Automated Identification of Incivility in Social Media",
author = "Davidson, Sam and
Sun, Qiusi and
Wojcieszak, Magdalena",
editor = "Akiwowo, Seyi and
Vidgen, Bertie and
Prabhakaran, Vinodkumar and
Waseem, Zeerak",
booktitle = "Proceedings of the Fourth Workshop on Online Abuse and Harms",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.alw-1.12",
doi = "10.18653/v1/2020.alw-1.12",
pages = "95--101",
abstract = "Incivility is not only prevalent on online social media platforms, but also has concrete effects on individual users, online groups, and the platforms themselves. Given the prevalence and effects of online incivility, and the challenges involved in human-based incivility detection, it is urgent to develop validated and versatile automatic approaches to identifying uncivil posts and comments. This project advances both a neural, BERT-based classifier as well as a logistic regression classifier to identify uncivil comments. The classifier is trained on a dataset of Reddit posts, which are annotated for incivility, and further expanded using a combination of labeled data from Reddit and Twitter. Our best performing model achieves an F1 of 0.802 on our Reddit test set. The final model is not only applicable across social media platforms and their distinct data structures, but also computationally versatile, and - as such - ready to be used on vast volumes of online data. All trained models and annotated data are made available to the research community.",
}
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<abstract>Incivility is not only prevalent on online social media platforms, but also has concrete effects on individual users, online groups, and the platforms themselves. Given the prevalence and effects of online incivility, and the challenges involved in human-based incivility detection, it is urgent to develop validated and versatile automatic approaches to identifying uncivil posts and comments. This project advances both a neural, BERT-based classifier as well as a logistic regression classifier to identify uncivil comments. The classifier is trained on a dataset of Reddit posts, which are annotated for incivility, and further expanded using a combination of labeled data from Reddit and Twitter. Our best performing model achieves an F1 of 0.802 on our Reddit test set. The final model is not only applicable across social media platforms and their distinct data structures, but also computationally versatile, and - as such - ready to be used on vast volumes of online data. All trained models and annotated data are made available to the research community.</abstract>
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%0 Conference Proceedings
%T Developing a New Classifier for Automated Identification of Incivility in Social Media
%A Davidson, Sam
%A Sun, Qiusi
%A Wojcieszak, Magdalena
%Y Akiwowo, Seyi
%Y Vidgen, Bertie
%Y Prabhakaran, Vinodkumar
%Y Waseem, Zeerak
%S Proceedings of the Fourth Workshop on Online Abuse and Harms
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F davidson-etal-2020-developing
%X Incivility is not only prevalent on online social media platforms, but also has concrete effects on individual users, online groups, and the platforms themselves. Given the prevalence and effects of online incivility, and the challenges involved in human-based incivility detection, it is urgent to develop validated and versatile automatic approaches to identifying uncivil posts and comments. This project advances both a neural, BERT-based classifier as well as a logistic regression classifier to identify uncivil comments. The classifier is trained on a dataset of Reddit posts, which are annotated for incivility, and further expanded using a combination of labeled data from Reddit and Twitter. Our best performing model achieves an F1 of 0.802 on our Reddit test set. The final model is not only applicable across social media platforms and their distinct data structures, but also computationally versatile, and - as such - ready to be used on vast volumes of online data. All trained models and annotated data are made available to the research community.
%R 10.18653/v1/2020.alw-1.12
%U https://aclanthology.org/2020.alw-1.12
%U https://doi.org/10.18653/v1/2020.alw-1.12
%P 95-101
Markdown (Informal)
[Developing a New Classifier for Automated Identification of Incivility in Social Media](https://aclanthology.org/2020.alw-1.12) (Davidson et al., ALW 2020)
ACL