[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122969-en":3,"doc-seo-122969-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122969,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Arabic Crime Tweet Filtering and Prediction Using Machine Learning","Crime is rising and harms national economies, making prediction efforts increasingly important. This study targets Arabic crime tweets on Twitter/X by analyzing social sentiment and extracting crime-related content through filtering. An intelligent dictionary is constructed with a genetic algorithm to capture crime behavior signals and reduce noise in the dataset. Multiple machine learning models are evaluated, including random forest, logistic regression, and decision trees, using accuracy, precision, recall, and F1 to ensure reliable performance. After dictionary-based filtering, achieved accuracies reach 97% for decision tree and random forest, and 94.43% for logistic regression.","Research Article  \nArabic Crime Tweet Filtering and Prediction Using Machine Learning  \nZainab Khyioon Abdalrdha1,*   \nProf. Dr. Abbas Mohsin Al-Bakry2   \nProf. Dr. Alaa K. Farhan3   \nInformatics Institute of Postgraduate Studies, Iraqi Commission for Computers & Informatics Baghdad, Iraq  \n[p](phd202120695@iips.edu.iq)[hd202120695@iips.edu.iq](phd202120695@iips.edu.iq)  \nUniversity of Information Technology and Communication Baghdad, Iraq  \n[abbasm.albakry@uoitc.edu.iq](abbasm.albakry@uoitc.edu.iq)  \nUniversity of Technology Department of Computer Sciences Baghdad, Iraq  \n[110030@uotechnology.edu.iq](110030@uotechnology.edu.iq)  \nA R T I C L E I N F O  \nArticle History  \nReceived: 01/03/2023  \nAccepted: 11/04/2024  \nPublished: 01/06/2024  \nThis is an open-access article under the CC BY 4.0 license:  \n[http://creativecommons.org/licenses/by/4](http://creativecommons.org/licenses/by/4) . 0/  \nABSTRACT  \nCrime is undeniably rising, thus negatively affecting countries’ economies. Despite several efforts to study crime prediction to reduce crime rates, few studies take the timeline factor into account when extracting crime-related tweets to predict crime. Aiming to predict Arabic crime tweets on Twitter/X, this study predicts crimes after analyzing social sentiment—that is, whether a tweet raises positive, negative, or neutral feelings—and filters the tweets based on crime behavior through an intelligent dictionary built through a genetic algorithm. The study uses a variety of machine learning (ML) models—random forest, logistic regression, and decision trees—which are assessed according to their accuracy, precision, recall, and F1 scores to guarantee robustness and dependability in crime prediction. The accuracy after filtering crimes based on an intelligent dictionary are 97% for decision tree, 97% for random forest, and 94.43% for logistic regression. This research will provide insight into potential crime attitudes and  \nopinion toward safety and law enforcement  \nKeywords: Cybercrime, Machine Learning, Twitter Analysis, Natural Language Processing (NLP), Random Forest, Logistic Regression  \n1. INTRODUCTION  \nSocial media serves various purposes but can also facilitate crimes [1] . Sharing personal information online can result in criminal activities. Victims might hesitate to report crimes because they consider them insignificant, feel embarrassed, or are unaware of the process. Social media monitoring can enhance traditional crime reporting. Social media is utilized to enable illegal activities, similar to other new technology and communication platforms [2]. Protecting private data during network transmissions is crucial [3] . Twitter is distinguished from other social networking sites by the fact that it allows users to submit news, thoughts, and ideas under a 280-character limit. In contrast to tweets, text relationships do not have the same method for sharing information [4][5] . Data dumps, security lapses, ransomware, vulnerabilities, DDoS assaults, zero-day exploits, and public events are a few instances of cyber threats that are regularly spoken about on Twitter, a platform heavily utilized for this kind of activity [6] . Researchers can use Twitter’s tracking and tweeting capabilities to assess interest in specific topics and uncover unforeseen cyber threats in real time [7] . Security intelligence utilizes artificial intelligence to gather and structure information related to cyber dangers [8] . Conventional machine learning (ML) algorithms have demonstrated efficacy in predicting crime. Several methods, including decision tree (DT), logistic regression (LR), and random forest (RF), have been used to analyze crime data to detect trends for predicting criminal behavior. Traditional ML models require less data and are simpler to interpret compared with deep learning, which depends on extensive data and intricate neural networks. This research suggests a method for forecasting Arabic crime tweets by utilizing ML model","cbCaig9xWFAQi81R","https://ap.wps.com/l/cbCaig9xWFAQi81R","pdf",1789228,1,13,"English","en",105,"# Abstract\n# Introduction\n# Related Work\n# Methodology\n# Experiments and Results\n# Conclusion and Future Work","[{\"question\":\"How does the study prepare data for Arabic crime tweet prediction?\",\"answer\":\"Tweets are analyzed for social sentiment and filtered using a genetic-algorithm-based intelligent dictionary to focus on crime-behavior signals.\"},{\"question\":\"Which machine learning models are used and how are they evaluated?\",\"answer\":\"Random forest, logistic regression, and decision trees are assessed with accuracy, precision, recall, and F1 scores to measure robustness and dependability.\"},{\"question\":\"What accuracy results are reported after filtering the tweets?\",\"answer\":\"After filtering with the intelligent dictionary, accuracy reaches 97% for decision tree, 97% for random forest, and 94.43% for logistic regression.\"}]","Arabic Crime Tweet Filtering and Prediction Using Machine Learning | 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does the study prepare data for Arabic crime tweet prediction?","Question",{"text":75,"@type":76},"Tweets are analyzed for social sentiment and filtered using a genetic-algorithm-based intelligent dictionary to focus on crime-behavior signals.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are used and how are they evaluated?",{"text":80,"@type":76},"Random forest, logistic regression, and decision trees are assessed with accuracy, precision, recall, and F1 scores to measure robustness and dependability.",{"name":82,"@type":73,"acceptedAnswer":83},"What accuracy results are reported after filtering the tweets?",{"text":84,"@type":76},"After filtering with the intelligent dictionary, accuracy reaches 97% for decision tree, 97% for random forest, and 94.43% for logistic 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