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Reporting the Unreported: Event Extraction for Analyzing the Local Representation of Hate Crimes
- Source :
- Scopus-Elsevier, EMNLP/IJCNLP (1)
- Publication Year :
- 2019
- Publisher :
- arXiv, 2019.
-
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.
- Subjects :
- FOS: Computer and information sciences
050103 clinical psychology
Computer Science - Computation and Language
Computer science
Event (relativity)
05 social sciences
Hate crime
Criminology
Representation (politics)
Computer Science - Computers and Society
03 medical and health sciences
0302 clinical medicine
Homicide
Computers and Society (cs.CY)
0501 psychology and cognitive sciences
Computation and Language (cs.CL)
030217 neurology & neurosurgery
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Scopus-Elsevier, EMNLP/IJCNLP (1)
- Accession number :
- edsair.doi.dedup.....c7eb01a79eeefa55f9ea0ada8c033adc
- Full Text :
- https://doi.org/10.48550/arxiv.1909.02126