Mafia language analysis and detection using Computational Linguistics tools

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/115809
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Title: Mafia language analysis and detection using Computational Linguistics tools
Authors: Morandini, Elena
Research Director: Lloret, Elena
Center, Department or Service: Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos
Keywords: Computational Linguistics | Criminal | Mafia | Language | Natural Language Processing tools | Machine Learning
Knowledge Area: Lenguajes y Sistemas Informáticos
Issue Date: 17-Jun-2021
Date of defense: 16-Jun-2021
Abstract: Hate Speech and violent language detection with Natural Language Processing (NLP) tools are up-to-theminute. If such technology could be applied to criminal jargon, it would give law enforcement agencies new resources to speed up investigations and evidence gathering against organised crime. This paper foresees the innovative approach of using computational tools and Machine Learning (ML) to detect Mafia language in electronic surveillance transcriptions used in Italian courts as evidence. Starting from a null hypothesis, this research will try to demonstrate the alternative hypothesis, that the variable Mafia and no-Mafia language can be differentiated. The first part of the investigation will determine the extent to which NLP tools detect Mafia language features, contrasting them with no-Mafia criminal jargon. The second part will describe an experiment to demonstrate how ML tools can recognise the Mafia variable for investigative purposes. In the conclusions, the effectiveness of the proposal as a pioneering method to fight the Mafia using its language will be discussed, defending the work of linguists and leaving the door open to new perspectives.
URI: http://hdl.handle.net/10045/115809
Language: eng
Type: info:eu-repo/semantics/masterThesis
Rights: Licencia Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0
Appears in Collections:Máster Universitario en Inglés y Español para fines Específicos - Trabajos Fin de Máster

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