@inproceedings{kennedy-etal-2017-technology,
title = "Technology Solutions to Combat Online Harassment",
author = "Kennedy, George and
McCollough, Andrew and
Dixon, Edward and
Bastidas, Alexei and
Ryan, John and
Loo, Chris and
Sahay, Saurav",
editor = "Waseem, Zeerak and
Chung, Wendy Hui Kyong and
Hovy, Dirk and
Tetreault, Joel",
booktitle = "Proceedings of the First Workshop on Abusive Language Online",
month = aug,
year = "2017",
address = "Vancouver, BC, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-3011",
doi = "10.18653/v1/W17-3011",
pages = "73--77",
abstract = "This work is part of a new initiative to use machine learning to identify online harassment in social media and comment streams. Online harassment goes under-reported due to the reliance on humans to identify and report harassment, reporting that is further slowed by requirements to fill out forms providing context. In addition, the time for moderators to respond and apply human judgment can take days, but response times in terms of minutes are needed in the online context. Though some of the major social media companies have been doing proprietary work in automating the detection of harassment, there are few tools available for use by the public. In addition, the amount of labeled online harassment data and availability of cross-platform online harassment datasets is limited. We present the methodology used to create a harassment dataset and classifier and the dataset used to help the system learn what harassment looks like.",
}
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%0 Conference Proceedings
%T Technology Solutions to Combat Online Harassment
%A Kennedy, George
%A McCollough, Andrew
%A Dixon, Edward
%A Bastidas, Alexei
%A Ryan, John
%A Loo, Chris
%A Sahay, Saurav
%Y Waseem, Zeerak
%Y Chung, Wendy Hui Kyong
%Y Hovy, Dirk
%Y Tetreault, Joel
%S Proceedings of the First Workshop on Abusive Language Online
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, BC, Canada
%F kennedy-etal-2017-technology
%X This work is part of a new initiative to use machine learning to identify online harassment in social media and comment streams. Online harassment goes under-reported due to the reliance on humans to identify and report harassment, reporting that is further slowed by requirements to fill out forms providing context. In addition, the time for moderators to respond and apply human judgment can take days, but response times in terms of minutes are needed in the online context. Though some of the major social media companies have been doing proprietary work in automating the detection of harassment, there are few tools available for use by the public. In addition, the amount of labeled online harassment data and availability of cross-platform online harassment datasets is limited. We present the methodology used to create a harassment dataset and classifier and the dataset used to help the system learn what harassment looks like.
%R 10.18653/v1/W17-3011
%U https://aclanthology.org/W17-3011
%U https://doi.org/10.18653/v1/W17-3011
%P 73-77
Markdown (Informal)
[Technology Solutions to Combat Online Harassment](https://aclanthology.org/W17-3011) (Kennedy et al., ALW 2017)
ACL
- George Kennedy, Andrew McCollough, Edward Dixon, Alexei Bastidas, John Ryan, Chris Loo, and Saurav Sahay. 2017. Technology Solutions to Combat Online Harassment. In Proceedings of the First Workshop on Abusive Language Online, pages 73–77, Vancouver, BC, Canada. Association for Computational Linguistics.