Detection of child exploiting chats from a mixed chat dataset as a text classification task (original) (raw)
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Providing a safe environment for juveniles and children in online social networks is considered as one of the major factors of improving public safety. Due to the prevalence of the online conversations, mitigating the undesirable effects of child abuse in cyber space has become inevitable. Using automatic ways to combat this kind of crime is challenging and demands efficient and scalable data mining techniques. The problem can be casted as a combination of textual preprocessing in data/text mining and pattern classification in machine learning. This chapter covers different data mining methods including preprocessing, feature extraction and the popular ways of feature enrichment through extracting sentiments and emotional features. A brief tutorial on classification algorithms in the domain of automated predator identification is also presented through the chapter. Finally, the discussion is summarized and the challenges and open issues in this application domain are discussed.
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Social media provides a powerful platform for individuals to communicate globally. This capability has many benefits, but it can also be used by malevolent individuals, i.e. predators. Anonymity exacerbates the problem. The motivation of our work is to help protect our children from this potentially hostile environment, without excluding them from utilizing its benefits. In our research, we aim to develop an online sexual predator identification system, designed to detect cybercrime related to child grooming. We will use AI techniques to analyze chat interactions available from different social networks. However, before any meaningful analysis can be carried out, chats must be preprocessed into a consistent and suitable format. This task poses challenges in itself. In this paper we show how different and diverse chat formats can be automatically normalized into a consistent text-based format that can be subsequently used for analysis.
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The internet especially social media has been a major platform where people interact with each other. We are able to interact with each other regardless of time and place because of the advancement of technology. Unfortunately, not all of the interaction that goes on are good or positive. One of the negative interaction that can happen online is cyberbullying which has rapidly increase throughout the years, whether it be through social media, emails or texting. Therefore, it is important to prevent cyberbullying from occurring which is why this research is done. Detection the presence of cyberbullying is one if the main issue in avoiding it from happening. Cyberbullying detection can be challenging because the many languages used in the world, most of the time slangs and informal languages are used and special characters like emoji are also used during online conversation. The aim of this research is to detect the presence of text cyberbullying from online post. Two term weighting s...
ChatBot Detection using Text Classification
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IRJET- Cyber Bullying Detection in Web Chat Application
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In today's life, the effect of social media is increasing popularly .cyber bullying has emerged as a critical afflicting children, youngster's and teenagers. System gaining knowledge of strategies make automatic detection of bullying messages in social media possible, and this may assist to assemble a healthful and safe social media environment. One critical issue in this significant research area is the reliable and discriminative processing of text messages in numerical representation. In this cyber bullying is harassment or bullying executed through digital device like computers, laptops, Smartphone and tablets. Cyber bullying can be defined as belligerent, intentional actions performed by an individual or a group of people via digital communication methods such as sending messages and posting comments against a victim. This paper, review Many companies will used this application for chatting, email notification, group chatting and meeting also. This web is helpful for people who is work on company because many people use the abusing word or comment on post that time ,this application doesn't show abused word only show there related symbols like asterisk, small square box etc. In this web, when person start chatting with another person or comment on post that time they cannot use abused words.
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023
The impact of social media on contemporary culture has been unprecedented, making it the most significant medium of our times. While it has had a positive effect on people's worldview, social media has also been linked to a rise in undesirable phenomena such as cyberbullying, cyberstalking, and cybercrime. Cyberbullying, in particular, can have a negative impact on individuals' mental health and has even been identified as the root cause of mental health issues in some cases. The proliferation of sexually explicit comments and the spread of rumors by multiple individuals are some of the negative influences that have been observed in the social media ecosystem. In recent years, academics have been increasingly concerned about the indicators of online harassment. Our goal is to develop a system that can detect instances of online abuse using Natural Language Processing (NLP) and Naïve Bayes, among other techniques. The cultural norms have shifted dramatically due to the rapid transmission of the COVID-19 virus, resulting in a rise in cyberbullying, especially among adolescents. The younger generation is more likely to engage in this practice, which has become more widespread with the stratospheric rise in popularity of various online engagement-promoting platforms. The COVID-19 pandemic has changed the way people interact online and has contributed to an increase in cyberbullying. As more people began working from home, bullying became a more significant concern. Our proposed system includes modules for data cleansing, text mining, word embedding, and regression analysis, among others. We utilize the Lemmatization technique for text mining, which enhances the model's precision. We also utilize the Vader emotion for feature extraction, which generates word vectors that are scattered numerical representations of word attributes. Additionally, Naive Bayes is used for data categorization to prevent overfitting in the proposed model. This would help in creating vectors that connect words with similar meanings.