Title |
Profiling School Shooters: Automatic Text-Based Analysis
|
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Published in |
Frontiers in Psychiatry, June 2015
|
DOI | 10.3389/fpsyt.2015.00086 |
Pubmed ID | |
Authors |
Yair Neuman, Dan Assaf, Yochai Cohen, James L. Knoll |
Abstract |
School shooters present a challenge to both forensic psychiatry and law enforcement agencies. The relatively small number of school shooters, their various characteristics, and the lack of in-depth analysis of all of the shooters prior to the shooting add complexity to our understanding of this problem. In this short paper, we introduce a new methodology for automatically profiling school shooters. The methodology involves automatic analysis of texts and the production of several measures relevant for the identification of the shooters. Comparing texts written by 6 school shooters to 6056 texts written by a comparison group of male subjects, we found that the shooters' texts scored significantly higher on the Narcissistic Personality dimension as well as on the Humilated and Revengeful dimensions. Using a ranking/prioritization procedure, similar to the one used for the automatic identification of sexual predators, we provide support for the validity and relevance of the proposed methodology. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 4 | 19% |
Australia | 2 | 10% |
Indonesia | 1 | 5% |
Switzerland | 1 | 5% |
Germany | 1 | 5% |
Belgium | 1 | 5% |
United States | 1 | 5% |
Unknown | 10 | 48% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 15 | 71% |
Scientists | 4 | 19% |
Practitioners (doctors, other healthcare professionals) | 1 | 5% |
Science communicators (journalists, bloggers, editors) | 1 | 5% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 66 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 13 | 20% |
Student > Bachelor | 12 | 18% |
Student > Master | 9 | 14% |
Researcher | 6 | 9% |
Student > Doctoral Student | 3 | 5% |
Other | 7 | 11% |
Unknown | 16 | 24% |
Readers by discipline | Count | As % |
---|---|---|
Psychology | 26 | 39% |
Social Sciences | 8 | 12% |
Medicine and Dentistry | 7 | 11% |
Computer Science | 2 | 3% |
Linguistics | 2 | 3% |
Other | 5 | 8% |
Unknown | 16 | 24% |