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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2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10704669/ |
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| author | Liu Yang Jiang Guofan Zhang Yixin Wei Qianze Zhang Jian Roohallah Alizadehsani Pawel Plawiak |
| author_facet | Liu Yang Jiang Guofan Zhang Yixin Wei Qianze Zhang Jian Roohallah Alizadehsani Pawel Plawiak |
| author_sort | Liu Yang |
| collection | DOAJ |
| 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 |
| id | doaj-art-00a76757a2ff456bbe402eff5bddc785 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-00a76757a2ff456bbe402eff5bddc7852025-08-20T01:47:58ZengIEEEIEEE Access2169-35362024-01-011214950214952710.1109/ACCESS.2024.347329610704669A Reinforcement Learning Approach Combined With Scope Loss Function for Crime Prediction on Twitter (X)Liu Yang0https://orcid.org/0009-0005-0853-5311Jiang Guofan1Zhang Yixin2Wei Qianze3Zhang Jian4https://orcid.org/0009-0002-2430-3818Roohallah Alizadehsani5https://orcid.org/0000-0003-0898-5054Pawel Plawiak6https://orcid.org/0000-0002-4317-2801Law School, Guangxi Police College, Nanning, ChinaDepartment of National Security, People's Public Security University of China, Beijing, ChinaBirmingham Law School, University of Birmingham, Birmingham, U.K.Department of Journalism and Communication, Hong Kong Chu Hai College, Tuen Mun, Hong KongSchool of Law, University of Malaya, Kuala Lumpur, MalaysiaInstitute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC, AustraliaDepartment of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Krakow, PolandOnline 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.https://ieeexplore.ieee.org/document/10704669/Crime predictionsentiment analysisreinforcement learningclass imbalancehyperparameter tuningartificial bee colony |
| spellingShingle | Liu Yang Jiang Guofan Zhang Yixin Wei Qianze Zhang Jian Roohallah Alizadehsani Pawel Plawiak A Reinforcement Learning Approach Combined With Scope Loss Function for Crime Prediction on Twitter (X) IEEE Access Crime prediction sentiment analysis reinforcement learning class imbalance hyperparameter tuning artificial bee colony |
| title | 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_short | 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 |
| topic | Crime prediction sentiment analysis reinforcement learning class imbalance hyperparameter tuning artificial bee colony |
| url | https://ieeexplore.ieee.org/document/10704669/ |
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