Detecting Nastiness in Social Media

Niloofar Safi Samghabadi, Suraj Maharjan, Alan Sprague, Raquel Diaz-Sprague, Thamar Solorio


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.
Anthology ID:
W17-3010
Volume:
Proceedings of the First Workshop on Abusive Language Online
Month:
August
Year:
2017
Address:
Vancouver, BC, Canada
Venues:
ALW | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
63–72
Language:
URL:
https://aclanthology.org/W17-3010
DOI:
10.18653/v1/W17-3010
Bibkey:
Cite (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.
Cite (Informal):
Detecting Nastiness in Social Media (Safi Samghabadi et al., 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-3010.pdf