Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

AI-Crime Hunter: An AI Mixture of Experts for Crime Discovery on Twitter

Version 1 : Received: 30 October 2021 / Approved: 1 November 2021 / Online: 1 November 2021 (15:25:19 CET)

A peer-reviewed article of this Preprint also exists.

Shoeibi, N.; Shoeibi, N.; Hernández, G.; Chamoso, P.; Corchado, J.M. AI-Crime Hunter: An AI Mixture of Experts for Crime Discovery on Twitter. Electronics 2021, 10, 3081. Shoeibi, N.; Shoeibi, N.; Hernández, G.; Chamoso, P.; Corchado, J.M. AI-Crime Hunter: An AI Mixture of Experts for Crime Discovery on Twitter. Electronics 2021, 10, 3081.

Journal reference: Electronics 2021, 10, 3081
DOI: 10.3390/electronics10243081

Abstract

Maintaining a healthy cyber society is a big challenge due to the users’ freedom of expression and behaving. It can be solved by monitoring and analyzing the users’ behavior and taking proper actions towards them. This research aims to present a platform that monitors the public content on Twitter by extracting tweet data. After maintaining the data, the users’ interactions are analyzed using Graph Analysis methods. Then the users’ behavioral patterns are analyzed by applying Metadata Analysis, in which the timeline of each profile is obtained; also, the time-series behavioral features of users are investigated. Then in the Abnormal Behavior Detection Filtering component, the interesting profiles are selected for further examinations. Finally, in the Contextual Analysis component, the contents will be analyzed using natural language processing techniques; A binary text classification model (SVM + TF-IDF with 88.89% accuracy) for detecting if the tweet is related to crime or not. Then, a sentiment analysis method is applied to the crime-related tweets to perform aspect-based sentiment analysis (DistilBERT + FFNN with 80% accuracy); because sharing positive opinions about a crime-related topic can threaten society. This platform aims to provide the end-user (Police) suggestions to control hate speech or terrorist propaganda.

Keywords

Twitter; Social Media Analysis; User Behavior Mining; Crime Detection; Feature Extraction; Graph Analysis; Natural Language Processing; Text Classification; Aspect-based Sentiment Analysis; DistilBERT

Subject

ENGINEERING, Other

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