@inproceedings{chang-etal-2018-detecting,
title = "Detecting Gang-Involved Escalation on Social Media Using Context",
author = "Chang, Serina and
Zhong, Ruiqi and
Adams, Ethan and
Lee, Fei-Tzin and
Varia, Siddharth and
Patton, Desmond and
Frey, William and
Kedzie, Chris and
McKeown, Kathy",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1005",
doi = "10.18653/v1/D18-1005",
pages = "46--56",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Detecting Gang-Involved Escalation on Social Media Using Context
%A Chang, Serina
%A Zhong, Ruiqi
%A Adams, Ethan
%A Lee, Fei-Tzin
%A Varia, Siddharth
%A Patton, Desmond
%A Frey, William
%A Kedzie, Chris
%A McKeown, Kathy
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F chang-etal-2018-detecting
%X 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.
%R 10.18653/v1/D18-1005
%U https://aclanthology.org/D18-1005
%U https://doi.org/10.18653/v1/D18-1005
%P 46-56
Markdown (Informal)
[Detecting Gang-Involved Escalation on Social Media Using Context](https://aclanthology.org/D18-1005) (Chang et al., EMNLP 2018)
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.