Detecting Gang-Involved Escalation on Social Media Using Context

Serina Chang, Ruiqi Zhong, Ethan Adams, Fei-Tzin Lee, Siddharth Varia, Desmond Patton, William Frey, Chris Kedzie, Kathy McKeown


Abstract
Gang-involved youth in cities such as Chicago have increasingly turned to social media to post about their experiences and intents online. In some situations, when they experience the loss of a loved one, their online expression of emotion may evolve into aggression towards rival gangs and ultimately into real-world violence. In this paper, we present a novel system for detecting Aggression and Loss in social media. Our system features the use of domain-specific resources automatically derived from a large unlabeled corpus, and contextual representations of the emotional and semantic content of the user’s recent tweets as well as their interactions with other users. Incorporating context in our Convolutional Neural Network (CNN) leads to a significant improvement.
Anthology ID:
D18-1005
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
46–56
Language:
URL:
https://aclanthology.org/D18-1005
DOI:
10.18653/v1/D18-1005
Bibkey:
Cite (ACL):
Serina Chang, Ruiqi Zhong, Ethan Adams, Fei-Tzin Lee, Siddharth Varia, Desmond Patton, William Frey, Chris Kedzie, and Kathy McKeown. 2018. Detecting Gang-Involved Escalation on Social Media Using Context. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 46–56, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Detecting Gang-Involved Escalation on Social Media Using Context (Chang et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1005.pdf
Attachment:
 D18-1005.Attachment.zip
Video:
 https://aclanthology.org/D18-1005.mp4
Code
 serinachang5/contextifier