Criminal Law in Protection of Health From the Abuse of Drugs in Serbia

Simović, Miodrag N.; Jovašević, Dragan; Simović, Marina M.

doi:10.35120/kij28061939m

Cited by 3 publications

(4 citation statements)

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“…The classification process used criminal tweets, with a total of 1,19597 tweets. Where the total number of tweets (1,19597), the number of positive tweets (60524), the number of similar tweets (50427), the number of bad tweets about criminal activity (67011), and the number of identical tweets (55254). The data set was divided into six parts, and 18,493 data sets were taken for training and testing in this paper.…”

Section: Twitter Data Extractionmentioning

confidence: 99%

“…Because of the complexities of the Arabic language, it is more difficult to detect crimes, which boosts human security. Crime impacts society through social media, with bullies targeting individuals based on race, body image, or religious beliefs [1]. Crime impacts individuals, society, and finances, requiring significant efforts for prevention, victim care, and legal proceedings to improve lives and security [2].…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid CNN-LSTM and XGBoost Approach for Crime Detection in Tweets Using an Intelligent Dictionary

Abdalrdha,

Al-Bakry,

Farhan

2023

RIA

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As social media grows, recognizing and managing illicit content, including threats, harassment, hate speech, armed robbery, drug smuggling, blackmail, and other crimes, is crucial. The present study uses machine learning and deep learning to create an intelligent lexicon for identifying crime-related material in Twitter tweets. The Aho-Corasick technique effectively creates a dictionary for extensive text corpus search, categorization, and keyword-based action execution. This strategy overcomes the evolution of language dynamics and criminal vocabulary to improve crime-related information detection. This paper aims to gather accurate data to help law enforcement identify and prevent specific crimes. For tweet preprocessing and extraction of relevant information like textual patterns and other distinctive qualities, natural language processing (NLP) technologies are prioritized. The paper describes labeling tweets into crime categories. This dataset trains supervised learning models to categorize tweets as criminal or not. XGBoost and Hybrid CNN-LSTM are combined for this. The suggested technique is assessed using precision, recall, F1-Score, accuracy, and MAP accuracy measures. These metrics measure the model's crime-related tweet identification accuracy. The Arabic tweets dataset, encompassing 18493 tweets and 10 features, is utilized for model testing. After training, the Hybrid CNN-LSTM model demonstrated an accuracy of 99.84% and a macro F1-Score of 98.20%. When the XGBoost method was employed, the traditional machine learning model achieved a peak F1 macro score of 99.36% and a maximum accuracy of 100%. The results suggest that while the deep learning models outperform machine learning models in the F1-Score, XGBoost exhibits superior accuracy. The paper presents a comprehensive strategy for crime detection in tweets, potentially offering a significant tool for law enforcement agencies.

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Section: Twitter Data Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid CNN-LSTM and XGBoost Approach for Crime Detection in Tweets Using an Intelligent Dictionary

Abdalrdha,

Al-Bakry,

Farhan

2023

RIA

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“…Social media is an easy way for people to communicate with others worldwide, and they can share their beliefs, interests, and many broad topics. However, unfortunately, the reach of these networks is extensive, and abusers can bully people according to their race, body image, or religious beliefs [3].…”

Section: Introductionmentioning

confidence: 99%

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

Shoeibi

Hernández

et al. 2021

Electronics

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Maintaining a healthy cyber society is a great challenge due to the users’ freedom of expression and behavior. This can be solved by monitoring and analyzing the users’ behavior and taking proper actions. 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 and filtering component, the interesting profiles are selected for further examinations. Finally, in the contextual analysis component, the contents are analyzed using natural language processing techniques; a binary text classification model (SVM (Support Vector Machine) + TF-IDF (Term Frequency—Inverse Document Frequency) with 88.89% accuracy) is used to detect if a 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 (Feed-Forward Neural Network) with 80% accuracy), because sharing positive opinions about a crime-related topic can threaten society. This platform aims to provide the end-user (the police) with suggestions to control hate speech or terrorist propaganda.

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“…However, unfortunately, It is extensive, And abusers can bully people by their race, Body image, religious beliefs. [3] Both victims and nonvictims can feel the lack of security, work Productivity, loss of money and property, And medical problems. For handling safe, People use extra protection, Spend lots of money to prevent crime.…”

Section: Introductionmentioning

confidence: 99%

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

Shoeibi¹,

Hernández²,

Chamoso³

et al. 2021

Preprint

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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.

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Criminal Law in Protection of Health From the Abuse of Drugs in Serbia

Cited by 3 publications

References 0 publications

A Hybrid CNN-LSTM and XGBoost Approach for Crime Detection in Tweets Using an Intelligent Dictionary

A Hybrid CNN-LSTM and XGBoost Approach for Crime Detection in Tweets Using an Intelligent Dictionary

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

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

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