Latent dirichlet allocation based blog analysis for criminal intention detection system

Chen, Sih-Huei; Santoso, Andri; Lee, Yuan-Shan; Wang, Jia-Ching

doi:10.1109/ccst.2015.7389660

Cited by 8 publications

(12 citation statements)

References 11 publications

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“…Mundra et al [69] studied embedding learning as a technique based on neural networks for classification of idiomatic expressions in microblogs (e.g., Twitter). In addition, the Latent Dirichlet Allocation and Collaborative representation classifiers were evaluated [5]. According to the authors, a crime intention detection system should combine more than one ML technique.…”

Section: Machine Learning and Mixed Learning Based Solutionsmentioning

confidence: 99%

“…To this end, automated services are necessary for both the prevention and investigative processes [3]. Nevertheless, it is still necessary to research more efficient methods to analyze the criminal content in the context of social networks [4], such as the analysis and detection of intentions related to crimes [5]. It is desirable, for example, to distinguish between who is inducing a person to commit a crime and who is commenting on a crime announced by the media.…”

Section: Introductionmentioning

confidence: 99%

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A Framework for Detecting Intentions of Criminal Acts in Social Media: A Case Study on Twitter

et al. 2020

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Criminals use online social networks for various activities by including communication, planning, and execution of criminal acts. They often employ ciphered posts using slang expressions, which are restricted to specific groups. Although literature shows advances in analysis of posts in natural language messages, such as hate discourses, threats, and more notably in the sentiment analysis; research enabling intention analysis of posts using slang expressions is still underexplored. We propose a framework and construct software prototypes for the selection of social network posts with criminal slang expressions and automatic classification of these posts according to illocutionary classes. The developed framework explores computational ontologies and machine learning (ML) techniques. Our defined Ontology of Criminal Expressions represents crime concepts in a formal and flexible model, and associates them with criminal slang expressions. This ontology is used for selecting suspicious posts and decipher them. In our solution, the criminal intention in written posts is automatically classified relying on learned models from existing posts. This work carries out a case study to evaluate the framework with 8,835,290 tweets. The obtained results show its viability by demonstrating the benefits in deciphering posts and the effectiveness of detecting user’s intention in written criminal posts based on ML.

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Section: Machine Learning and Mixed Learning Based Solutionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Framework for Detecting Intentions of Criminal Acts in Social Media: A Case Study on Twitter

et al. 2020

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“…Beyond the analyses of free-text recorded by police, several studies have also explored how LDA topic models might be used to analyse crime related phenomena discussed in unstructured content posted online. Chen et al (2015) combine LDA and collaborative representation classifiers in an attempt to detect 'criminal intention' in free-text extracted from online blogs. Relatedly, Gerber (2014) apply LDA topic models to geotagged tweets posted to the online platform Twitter in Chicago to generate neighbourhood dominant topics indicative of the ecology of a particular location.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised identification of crime problems from police free-text data

Birks

Coleman

Jackson³

2020

Crime Sci

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We present a novel exploratory application of unsupervised machine-learning methods to identify clusters of specific crime problems from unstructured modus operandi free-text data within a single administrative crime classification. To illustrate our proposed approach, we analyse police recorded free-text narrative descriptions of residential burglaries occurring over a two-year period in a major metropolitan area of the UK. Results of our analyses demonstrate that topic modelling algorithms are capable of clustering substantively different burglary problems without prior knowledge of such groupings. Subsequently, we describe a prototype dashboard that allows replication of our analytical workflow and could be applied to support operational decision making in the identification of specific crime problems. This approach to grouping distinct types of offences within existing offence categories, we argue, has the potential to support crime analysts in proactively analysing large volumes of modus operandi free-text data—with the ultimate aims of developing a greater understanding of crime problems and supporting the design of tailored crime reduction interventions.

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“…Some researches applied topic modeling methods in the field of crime prediction and activity analysis. Chen et al [24] developed an early warning system based on LDA and a collaborative representation classifier to detect criminal activity intentions. Sharma et al [25] introduced a crime intensity geographic model to detect the safest path between two locations, which employs a naive Bayes classifier with features derived from the LDA model.…”

Section: A Topic Models In Applicationsmentioning

confidence: 99%

Collaboratively Modeling and Embedding of Latent Topics for Short Texts

et al. 2020

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Deriving a successful document representation is the critical challenge in many downstream tasks in NLP, especially when documents are very short. It is challenging to handle the sparsity and the noise problems confronting short texts. Some approaches employ latent topic models, based on global word co-occurrence, to obtain topic distribution as the representation. Others leverage word embeddings, which consider local conditional dependencies, to map a document as a summation vector of them. Unlike the existing works which explore the strategy of utilizing one to help the other, i.e., topic models for word embeddings or vice versa, we propose CME-DMM, a collaboratively modeling and embedding framework for capturing coherent latent topics from short texts. CME-DMM incorporates topic and word embeddings through the attention mechanism and implants them into the latent topic models, which significantly improve the quality of latent topics. Extensive experiments demonstrate that CME-DMM could perceive more coherent topics than other popular methods, resulting in a better performance in downstream NLP tasks such as classification. Besides the interpretable latent topics, the corresponding topic embeddings can describe the meanings of latent topics in the semantic space. The attention vectors, as a by-product of the learning process, can identify the keywords in noisy short texts.

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Latent dirichlet allocation based blog analysis for criminal intention detection system

Cited by 8 publications

References 11 publications

A Framework for Detecting Intentions of Criminal Acts in Social Media: A Case Study on Twitter

A Framework for Detecting Intentions of Criminal Acts in Social Media: A Case Study on Twitter

Unsupervised identification of crime problems from police free-text data

Collaboratively Modeling and Embedding of Latent Topics for Short Texts

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