@inproceedings{safi-samghabadi-etal-2017-detecting,
title = "Detecting Nastiness in Social Media",
author = "Safi Samghabadi, Niloofar and
Maharjan, Suraj and
Sprague, Alan and
Diaz-Sprague, Raquel and
Solorio, Thamar",
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/W17-3010",
doi = "10.18653/v1/W17-3010",
pages = "63--72",
abstract = "Although social media has made it easy for people to connect on a virtually unlimited basis, it has also opened doors to people who misuse it to undermine, harass, humiliate, threaten and bully others. There is a lack of adequate resources to detect and hinder its occurrence. In this paper, we present our initial NLP approach to detect invective posts as a first step to eventually detect and deter cyberbullying. We crawl data containing profanities and then determine whether or not it contains invective. Annotations on this data are improved iteratively by in-lab annotations and crowdsourcing. We pursue different NLP approaches containing various typical and some newer techniques to distinguish the use of swear words in a neutral way from those instances in which they are used in an insulting way. We also show that this model not only works for our data set, but also can be successfully applied to different data sets.",
}
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%0 Conference Proceedings
%T Detecting Nastiness in Social Media
%A Safi Samghabadi, Niloofar
%A Maharjan, Suraj
%A Sprague, Alan
%A Diaz-Sprague, Raquel
%A Solorio, Thamar
%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 safi-samghabadi-etal-2017-detecting
%X Although social media has made it easy for people to connect on a virtually unlimited basis, it has also opened doors to people who misuse it to undermine, harass, humiliate, threaten and bully others. There is a lack of adequate resources to detect and hinder its occurrence. In this paper, we present our initial NLP approach to detect invective posts as a first step to eventually detect and deter cyberbullying. We crawl data containing profanities and then determine whether or not it contains invective. Annotations on this data are improved iteratively by in-lab annotations and crowdsourcing. We pursue different NLP approaches containing various typical and some newer techniques to distinguish the use of swear words in a neutral way from those instances in which they are used in an insulting way. We also show that this model not only works for our data set, but also can be successfully applied to different data sets.
%R 10.18653/v1/W17-3010
%U https://aclanthology.org/W17-3010
%U https://doi.org/10.18653/v1/W17-3010
%P 63-72
Markdown (Informal)
[Detecting Nastiness in Social Media](https://aclanthology.org/W17-3010) (Safi Samghabadi et al., ALW 2017)
ACL
- Niloofar Safi Samghabadi, Suraj Maharjan, Alan Sprague, Raquel Diaz-Sprague, and Thamar Solorio. 2017. Detecting Nastiness in Social Media. In Proceedings of the First Workshop on Abusive Language Online, pages 63–72, Vancouver, BC, Canada. Association for Computational Linguistics.