Abusive Language Detection in Online User Content [Yahoo Labs]
http://www2016.net/proceedings/proceedings/p145.pdf
Detection of abusive language in user generated online content
has become an issue of increasing importance in recent years.
Most current commercial methods make use of blacklists and
regular expressions, however these measures fall short when
contending with more subtle, less ham-fisted examples of hate
speech. In this work, we develop a machine learning based
method to detect hate speech on online user comments from two
domains which outperforms a state-ofthe-art deep learning
approach. We also develop a corpus of user comments annotated
for abusive language, the first of its kind. Finally, we use
our detection tool to analyze abusive language over time and
in different settings to further enhance our knowledge of this
behavior.
- - -
--Lauren--
Lauren Weinstein ([email protected]): https://www.vortex.com/lauren
Founder:
- Network Neutrality Squad: https://www.nnsquad.org
- PRIVACY Forum: https://www.vortex.com/privacy-info
Co-Founder: People For Internet Responsibility: https://www.pfir.org/pfir-info
Member: ACM Committee on Computers and Public Policy
Lauren's Blog: https://lauren.vortex.com
Google+: https://google.com/+LaurenWeinstein
Twitter: https://twitter.com/laurenweinstein
Tel: +1 (818) 225-2800 / Skype: vortex.com
I have consulted to Google, but I am not currently
doing so -- my opinions expressed here are mine alone.
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