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A Reinforcement Learning Approach Combined With Scope Loss Function for Crime Prediction on Twitter (X)

Online social networks, especially Twitter (X), have become focal points for illicit activities, providing unique criminal investigation opportunities. This paper introduces an innovative methodology that uses social media sentiment analysis to predict criminal activities. One major challenge in sen...

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Main Authors: Liu, Yang, Jiang, Guofan, Zhang, Yixin, Wei, Qianze, Zhang, Jian, Alizadehsani, Roohallah, Plawiak, Pawel
Format: Article
Published: Institute of Electrical and Electronics Engineers 2024
Subjects:
K Law (General)
QA75 Electronic computers. Computer science
Online Access:http://eprints.um.edu.my/47096/
https://doi.org/10.1109/ACCESS.2024.3473296
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spelling my.um.eprints.470962024-11-22T04:54:06Z http://eprints.um.edu.my/47096/ A Reinforcement Learning Approach Combined With Scope Loss Function for Crime Prediction on Twitter (X) Liu, Yang Jiang, Guofan Zhang, Yixin Wei, Qianze Zhang, Jian Alizadehsani, Roohallah Plawiak, Pawel K Law (General) QA75 Electronic computers. Computer science Online social networks, especially Twitter (X), have become focal points for illicit activities, providing unique criminal investigation opportunities. This paper introduces an innovative methodology that uses social media sentiment analysis to predict criminal activities. One major challenge in sentiment analysis is the uneven distribution of sentiment classes, where traditional models often fail to accurately classify instances of the minority class due to the overwhelming presence of majority class data. To tackle this issue, we propose a model that combines a reinforcement learning (RL) algorithm with a scope loss function. The RL approach uses a reward mechanism that assigns a more significant value to correctly predicting minority class instances over majority class ones. The scope loss function ensures an optimal balance between utilizing known data and exploring new data, thus maintaining a delicate equilibrium between accuracy and generalizability. Our model employs a series of convolutional neural networks (CNNs) to extract significant features from textual content, which are then utilized for sentiment classification. We also incorporate an advanced artificial bee colony (ABC) optimization technique to refine the model's hyperparameters. The effectiveness of our approach was empirically tested using two distinct datasets: one consisting of crime incident reports from the Chicago Police Department covering the period from September 2019 to July 2024 and another comprising tweets containing crime-related terms related to Chicago. The predictive capabilities of our proposed model were benchmarked against existing models, demonstrating superior performance with accuracies of 96.411% and 94.088%, respectively. This breakthrough highlights the potential of integrating sentiment analysis with reinforcement learning to significantly enhance the predictive accuracy of crime-related activities in online social networks, offering valuable insights for law enforcement and criminal investigation applications. Institute of Electrical and Electronics Engineers 2024 Article PeerReviewed Liu, Yang and Jiang, Guofan and Zhang, Yixin and Wei, Qianze and Zhang, Jian and Alizadehsani, Roohallah and Plawiak, Pawel (2024) A Reinforcement Learning Approach Combined With Scope Loss Function for Crime Prediction on Twitter (X). IEEE Access, 12. pp. 149502-149527. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2024.3473296 <https://doi.org/10.1109/ACCESS.2024.3473296>. https://doi.org/10.1109/ACCESS.2024.3473296 10.1109/ACCESS.2024.3473296
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic K Law (General)
QA75 Electronic computers. Computer science
spellingShingle K Law (General)
QA75 Electronic computers. Computer science
Liu, Yang
Jiang, Guofan
Zhang, Yixin
Wei, Qianze
Zhang, Jian
Alizadehsani, Roohallah
Plawiak, Pawel
A Reinforcement Learning Approach Combined With Scope Loss Function for Crime Prediction on Twitter (X)
description Online social networks, especially Twitter (X), have become focal points for illicit activities, providing unique criminal investigation opportunities. This paper introduces an innovative methodology that uses social media sentiment analysis to predict criminal activities. One major challenge in sentiment analysis is the uneven distribution of sentiment classes, where traditional models often fail to accurately classify instances of the minority class due to the overwhelming presence of majority class data. To tackle this issue, we propose a model that combines a reinforcement learning (RL) algorithm with a scope loss function. The RL approach uses a reward mechanism that assigns a more significant value to correctly predicting minority class instances over majority class ones. The scope loss function ensures an optimal balance between utilizing known data and exploring new data, thus maintaining a delicate equilibrium between accuracy and generalizability. Our model employs a series of convolutional neural networks (CNNs) to extract significant features from textual content, which are then utilized for sentiment classification. We also incorporate an advanced artificial bee colony (ABC) optimization technique to refine the model's hyperparameters. The effectiveness of our approach was empirically tested using two distinct datasets: one consisting of crime incident reports from the Chicago Police Department covering the period from September 2019 to July 2024 and another comprising tweets containing crime-related terms related to Chicago. The predictive capabilities of our proposed model were benchmarked against existing models, demonstrating superior performance with accuracies of 96.411% and 94.088%, respectively. This breakthrough highlights the potential of integrating sentiment analysis with reinforcement learning to significantly enhance the predictive accuracy of crime-related activities in online social networks, offering valuable insights for law enforcement and criminal investigation applications.
format Article
author Liu, Yang
Jiang, Guofan
Zhang, Yixin
Wei, Qianze
Zhang, Jian
Alizadehsani, Roohallah
Plawiak, Pawel
author_facet Liu, Yang
Jiang, Guofan
Zhang, Yixin
Wei, Qianze
Zhang, Jian
Alizadehsani, Roohallah
Plawiak, Pawel
author_sort Liu, Yang
title A Reinforcement Learning Approach Combined With Scope Loss Function for Crime Prediction on Twitter (X)
title_short A Reinforcement Learning Approach Combined With Scope Loss Function for Crime Prediction on Twitter (X)
title_full A Reinforcement Learning Approach Combined With Scope Loss Function for Crime Prediction on Twitter (X)
title_fullStr A Reinforcement Learning Approach Combined With Scope Loss Function for Crime Prediction on Twitter (X)
title_full_unstemmed A Reinforcement Learning Approach Combined With Scope Loss Function for Crime Prediction on Twitter (X)
title_sort reinforcement learning approach combined with scope loss function for crime prediction on twitter (x)
publisher Institute of Electrical and Electronics Engineers
publishDate 2024
url http://eprints.um.edu.my/47096/
https://doi.org/10.1109/ACCESS.2024.3473296
_version_ 1817841974787440640
score 13.235362

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