441 research outputs found
Integrated-system to minimizing cyber harassment in kingdom of Saudi Arabia (KSA)
The proposed system framework consists two main databases: Lexicon dictionary and Summarized previous cases, by depending on Sentiment analysis and N-Gram algorithms to match the terms and documents. In the first branch, the judge opens the cyber case and therefore the system will highlight the technical terms automatically. Furthermore, the technical terms matched with Lexicon dictionary will be highlighted. After that, the judge opens the highlighted terms (as links), and description page will be appeared. The description page contains details about the technical terms (definitions, explanations, examples, etc). On the other side, the second branch aims to retrieve the related legal cases (from the database) judged by courts in UK and KSA. The related cases are the most closed cases to the current legal case by inserting keywords based on the current case. The judge benefits from these cases through the judgment issued to give the fair judgment. N-gram algorithm is used to find the related cases because it has smart approach to expect the most closed document and texts. The system provides the judge with laws used in issuing the judgment in KSA and UK courts
Determining Bullying Text Classification Using Naive Bayes Classification on Social Media
Cyber-bullying includes repeated acts with the aim of scaring, angering, or embarrassing those who are targeted Cyber-bullying is happening along with the rapid development of technology and social media in society. The media and users need to filter out bully comments because they can indirectly affect the mental psychology that reads them especially directly aimed at that person. By utilizing information mining, the system is expected to be able to classify information circulating in the community. One of the classification techniques that can be applied to text-based classification is Naïve Bayes. The algorithm is good at performing the classification process. In this research, the precision of the algorithm's has been carried out on 1000 comment datasets. The data is grouped manually first into the labels "bully" and "not bully" then the data is divided into training data and test data. To test the system's ability, the classified data is analyzed using the confusion matrix method. The results showed that the Naïve Bayes Algorithm got the level of precision at 87%. and the level of area under the curve (AUC) at 88%. In terms of speed of completing the system, the Naïve Bayes Algorithm has a very good rate of speed with completion time of 0.033 seconds
Digital Safety During Online Learning: What We Do to Protect Our Student?
The article aims to uncover security risks that may occur during online learning, as well as preventive measures that can be taken to avoid these threats. This study uses a combination of two methods, namely, web mining and literature review. From the results of web mining, it is found that the website articles have not provided much explanation about efforts to protect against threats on the internet, from the results of the literature review the researchers revealed that several threats that can occur on the internet, namely phishing, scamming, fraud, cyberbullying, viruses, privacy and personal data issues, and obscene or pornographic content. This study also provides three important steps in protecting children from internet threats during online learning, including assistance in accessing internet content, education about internet safety and personal data protection, as well as an introduction to Digital Citizenship and ethics in cyberspace.
Keywords: Digital Safety, Online Learning Safety, Digital Citizenshi
Exploring Text Mining and Analytics for Applications in Public Security: An in-depth dive into a systematic literature review
Text mining and related analytics emerge as a technological approach to support human activities in extracting useful knowledge through texts in several formats. From a managerial point of view, it can help organizations in planning and decision-making processes, providing information that was not previously evident through textual materials produced internally or even externally. In this context, within the public/governmental scope, public security agencies are great beneficiaries of the tools associated with text mining, in several aspects, from applications in the criminal area to the collection of people's opinions and sentiments about the actions taken to promote their welfare. This article reports details of a systematic literature review focused on identifying the main areas of text mining application in public security, the most recurrent technological tools, and future research directions. The searches covered four major article bases (Scopus, Web of Science, IEEE Xplore, and ACM Digital Library), selecting 194 materials published between 2014 and the first half of 2021, among journals, conferences, and book chapters. There were several findings concerning the targets of the literature review, as presented in the results of this article
Multilingual Cyberbullying Detection System
Indiana University-Purdue University Indianapolis (IUPUI)Since the use of social media has evolved, the ability of its users to bully others has increased. One of the prevalent forms of bullying is Cyberbullying, which occurs on the social media sites such as Facebook©, WhatsApp©, and Twitter©. The past decade has witnessed a growth in cyberbullying – is a form of bullying that occurs virtually by the use of electronic devices, such as messaging, e-mail, online gaming, social media, or through images or mails sent to a mobile. This bullying is not only limited to English language and occurs in other languages. Hence, it is of the utmost importance to detect cyberbullying in multiple languages. Since current approaches to identify cyberbullying are mostly focused on English language texts, this thesis proposes a new approach (called Multilingual Cyberbullying Detection System) for the detection of cyberbullying in multiple languages (English, Hindi, and Marathi). It uses two techniques, namely, Machine Learning-based and Lexicon-based, to classify the input data as bullying or non-bullying. The aim of this research is to not only detect cyberbullying but also provide a distributed infrastructure to detect bullying. We have developed multiple prototypes (standalone, collaborative, and cloud-based) and carried out experiments with them to detect cyberbullying on different datasets from multiple languages. The outcomes of our experiments show that the machine-learning model outperforms the lexicon-based model in all the languages. In addition, the results of our experiments show that collaboration techniques can help to improve the accuracy of a poor-performing node in the system. Finally, we show that the cloud-based configurations performed better than the local configurations
Hubungan antara Cyberbullying dengan Kenakalan Remaja
The use of the internet cannot replace from everyday life. UNICEF research results show internet users in Indonesia who come from among children and adolescents are predicted to reach 30 million. The development of information technology has made cyberspace a place for online violence or cyberbullying. This study aims to find out the relationship between cyberbullying with juvenile delinquency. This study used a descriptive correlational design with a cross sectional design. Data were analyzed with the Spearman Rank trial. The study showed that the correlation between cyberbullying with juvenile delinquency is very weak with the value of the coefficient of weakness of 0.161. This result is also strengthened by the significance value of 0.78 which means that the significance value α (0.05). It can be concluded based on statistics that there is no relationship between cyberbullying with juvenile delinquency
A Survey on Cybercrime Using Social Media
There is growing interest in automating crime detection and prevention for large populations as a result of the increased usage of social media for victimization and criminal activities. This area is frequently researched due to its potential for enabling criminals to reach a large audience. While several studies have investigated specific crimes on social media, a comprehensive review paper that examines all types of social media crimes, their similarities, and detection methods is still lacking. The identification of similarities among crimes and detection methods can facilitate knowledge and data transfer across domains. The goal of this study is to collect a library of social media crimes and establish their connections using a crime taxonomy. The survey also identifies publicly accessible datasets and offers areas for additional study in this area
Cyberbullying Analysis on Instagram Using K-Means Clustering
Social Media, in addition to having a positive impact on society, also has a negative effect. Based on statistics, 95 percent of internet users in Indonesia use the internet to access social networks. Especially for young people, Instagram is more widely used than other social media such as Twitter and Facebook. In terms of cyberbullying cases, cases often occur through social media, Twitter, and Instagram. Several methods are commonly used to analyze cyberbullying cases, such as SVM (Support Vector Machine), NBC (Naïve Bayes Classifier), C45, and K-Nearest Neighbors. Application of a number of these methods is generally implemented on Twitter social media. Meanwhile, young users currently use Instagram more social media than Twitter. For this reason, the research focuses on analyzing cyberbullying on Instagram by applying the K-Mean Clustering algorithm. This algorithm is used to classify cyberbullying actions contained in comments. The dataset used in this study was taken from 2019 to 2021 with 650 records; there were 1827 words and already had labels. This study has successfully classified the tested data with a threshold value of 0.5. The results for grouping words containing bullying on Instagram resulted in the highest accuracy, which is 67.38%, a precision value of 76.70%, and a recall value of 67.48%. These results indicate that the k-means algorithm can make a grouping of comments into two clusters: bullying and non-bullying
Violence Detection in Social Media-Review
Social media has become a vital part of humans’ day to day life. Different users engage with social media differently. With the increased usage of social media, many researchers have investigated different aspects of social media. Many examples in the recent past show, content in the social media can generate violence in the user community. Violence in social media can be categorised into aggregation in comments, cyber-bullying and incidents like protests, murders. Identifying violent content in social media is a challenging task: social media posts contain both the visual and text as well as these posts may contain hidden meaning according to the users’ context and other background information. This paper summarizes the different social media violent categories and existing methods to detect the violent content.Keywords: Machine learning, natural language processing, violence, social media, convolution neural networ
- …