@inproceedings{mostafazadeh-davani-etal-2019-reporting,
title = "Reporting the Unreported: Event Extraction for Analyzing the Local Representation of Hate Crimes",
author = "Mostafazadeh Davani, Aida and
Yeh, Leigh and
Atari, Mohammad and
Kennedy, Brendan and
Portillo Wightman, Gwenyth and
Gonzalez, Elaine and
Delong, Natalie and
Bhatia, Rhea and
Mirinjian, Arineh and
Ren, Xiang and
Dehghani, Morteza",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1580",
doi = "10.18653/v1/D19-1580",
pages = "5753--5757",
abstract = "Official reports of hate crimes in the US are under-reported relative to the actual number of such incidents. Further, despite statistical approximations, there are no official reports from a large number of US cities regarding incidents of hate. Here, we first demonstrate that event extraction and multi-instance learning, applied to a corpus of local news articles, can be used to predict instances of hate crime. We then use the trained model to detect incidents of hate in cities for which the FBI lacks statistics. Lastly, we train models on predicting homicide and kidnapping, compare the predictions to FBI reports, and establish that incidents of hate are indeed under-reported, compared to other types of crimes, in local press.",
}
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<abstract>Official reports of hate crimes in the US are under-reported relative to the actual number of such incidents. Further, despite statistical approximations, there are no official reports from a large number of US cities regarding incidents of hate. Here, we first demonstrate that event extraction and multi-instance learning, applied to a corpus of local news articles, can be used to predict instances of hate crime. We then use the trained model to detect incidents of hate in cities for which the FBI lacks statistics. Lastly, we train models on predicting homicide and kidnapping, compare the predictions to FBI reports, and establish that incidents of hate are indeed under-reported, compared to other types of crimes, in local press.</abstract>
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%0 Conference Proceedings
%T Reporting the Unreported: Event Extraction for Analyzing the Local Representation of Hate Crimes
%A Mostafazadeh Davani, Aida
%A Yeh, Leigh
%A Atari, Mohammad
%A Kennedy, Brendan
%A Portillo Wightman, Gwenyth
%A Gonzalez, Elaine
%A Delong, Natalie
%A Bhatia, Rhea
%A Mirinjian, Arineh
%A Ren, Xiang
%A Dehghani, Morteza
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F mostafazadeh-davani-etal-2019-reporting
%X Official reports of hate crimes in the US are under-reported relative to the actual number of such incidents. Further, despite statistical approximations, there are no official reports from a large number of US cities regarding incidents of hate. Here, we first demonstrate that event extraction and multi-instance learning, applied to a corpus of local news articles, can be used to predict instances of hate crime. We then use the trained model to detect incidents of hate in cities for which the FBI lacks statistics. Lastly, we train models on predicting homicide and kidnapping, compare the predictions to FBI reports, and establish that incidents of hate are indeed under-reported, compared to other types of crimes, in local press.
%R 10.18653/v1/D19-1580
%U https://aclanthology.org/D19-1580
%U https://doi.org/10.18653/v1/D19-1580
%P 5753-5757
Markdown (Informal)
[Reporting the Unreported: Event Extraction for Analyzing the Local Representation of Hate Crimes](https://aclanthology.org/D19-1580) (Mostafazadeh Davani et al., EMNLP-IJCNLP 2019)
ACL
- Aida Mostafazadeh Davani, Leigh Yeh, Mohammad Atari, Brendan Kennedy, Gwenyth Portillo Wightman, Elaine Gonzalez, Natalie Delong, Rhea Bhatia, Arineh Mirinjian, Xiang Ren, and Morteza Dehghani. 2019. Reporting the Unreported: Event Extraction for Analyzing the Local Representation of Hate Crimes. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5753–5757, Hong Kong, China. Association for Computational Linguistics.