Topic modeling approaches to counter online grooming-Reference-Cited by-同舟云学术

Topic modeling approaches to counter online grooming

Published:2022-06-26 Issue: Volume: Page:
ISSN:
Container-title:14th ACM Web Science Conference 2022
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Author:

Lykousas Nikolaos¹,Patsakis Constantinos²

Affiliation:

1. Department of Informatics, University of Piraeus, Greece

2. Department of Informatics, University of Piraeus, Greece and Information Management Systems Institute, Athena Research Centre, Greece

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3501247.3539507

Reference27 articles.

1. Alexander Babuta Marion Oswald and Christine Rinik. 2018. Machine learning algorithms and police decision-making: legal ethical and regulatory challenges. (2018). Alexander Babuta Marion Oswald and Christine Rinik. 2018. Machine learning algorithms and police decision-making: legal ethical and regulatory challenges. (2018).

2. Abdur Rahman MA Basher and Benjamin CM Fung . 2014. Analyzing topics and authors in chat logs for crime investigation. Knowledge and information systems 39, 2 ( 2014 ), 351–381. Abdur Rahman MA Basher and Benjamin CM Fung. 2014. Analyzing topics and authors in chat logs for crime investigation. Knowledge and information systems 39, 2 (2014), 351–381.

3. Marcio Pereira Basilio Gabrielle Souza Brum and Valdecy Pereira. 2020. A model of policing strategy choice: the integration of the Latent Dirichlet Allocation (LDA) method with ELECTRE I. Journal of Modelling in Management(2020). Marcio Pereira Basilio Gabrielle Souza Brum and Valdecy Pereira. 2020. A model of policing strategy choice: the integration of the Latent Dirichlet Allocation (LDA) method with ELECTRE I. Journal of Modelling in Management(2020).

4. Unsupervised identification of crime problems from police free-text data

5. Latent dirichlet allocation;Blei M;Journal of machine Learning research 3,2003