Harassment Detection Research Articles

Modified Metaheuristics Optimization for Cyberbullying Detection on Online Data Science Platform

Online harassment detection faces significant challenges due to its expansive reach and anonymity. Addressing this issue demands effective detection mechanisms capable of processing vast data streams and adapting to evolving online language. Leveraging advancements in artificial intelligence, we propose a novel approach grounded in natural language processing (NLP) and metaheuristic algorithms. Our methodology integrates term frequency-inverse document frequency (TF-IDF) encoding and the AdaBoost algorithm for classification. To tackle the NP-hard problem of hyperparameter selection, we introduce a modified crayfish optimization algorithm (COA), termed GI-COA. This paper represents a pioneering effort in utilizing metaheuristic algorithms for hyperparameter selection in harassment detection models. Through experimentation, we demonstrate the efficacy of our approach in fostering a safer online environment. The best performing optimize models demonstrate and accuracy exceeding 77%.

A Review Paper on Cyber Harassment Detection Using Machine Learning Algorithm on Social Networking Website

Abstract: Cyber bullying is nothing but bullying with the use of digital technology. Cyber bullying can take place on social media platforms, messaging platforms, gaming platforms and mobile phones etc. Cyber bullying is a big issue that is encountered by the individual on internet that affects teenagers as well as adults. It has lead to nuisances like suicide, mental health problems and depression. Therefore regulation of content on Social media platforms has become a growing need. Also detecting Cyber bullying detection at early stages can help to alleviate impacts on the victims. In the following research we make use of data from two different data sets. The first one is tweets from Twitter and second one is comments based on personal attacks from Wikipedia forums. The approach is to build a model based on detection of cyber bullying in text data using Natural Language Processing and Machine learning. We try to build a model which will provide accuracy up to 90% for tweets and accuracy up to 80% for Wikipedia forums. Cyber bullying detection will be done as a binary classification problem where we are detecting two major form of cyber bullying: hate speech on Twitter and Personal attacks on Wikipedia and classifying them as containing Cyber bullying or not. The end result of this proposed work may reduce negative impacts on the victims as a result of early detection.

Identification of cyber harassment and intention of target users on social media platforms

Due to Coronavirus diseases in 2020, all the countries departed into lockdown to combat the spread of the pandemic situation. Schools and institutions remain closed and students’ screen time surged. The classes for the students are moved to the digital platform which leads to an increase in social media usage. Many children had become sufferers of cyber harassment which includes threatening comments on young students, sexual torture through a digital platform, people insulting one another, and the use of fake accounts to harass others. The rising effort on automated cyber harassment detection utilizes many AI-related components Natural language processing techniques and machine learning approaches. Though machine learning models using different algorithms fail to converge with higher accuracy, it is much more important to use significant natural language processes and efficient classifiers to detect cyberbullying comments on social media. In this proposed work, the lexical meaning of the text is analysed by the conventional scheme and the word order of the text is performed by the Fast Text model to improve the computational efficacy of the model. The intention of the text is analysed by various feature extraction methods. The score for intention detection is calculated using the frequency of words with a bully-victim participation score. Finally, the proposed model’s performance is measured by different evaluation metrics which illustrate that the accuracy of the model is higher than many other existing classification methods. The error rate is lesser for the detection model.

Cyber Harassment Detection on Social Network Using Deep Convolutional Neural Network

Abstract: Rate of innovation is increasing apace. This growth in innovation provides another dimension of communication. However, in spite of the very fact that innovation conjures up U.S. in numerous elements of life, it goes with completely different effects that influence folks during a hardly or the opposite means. Cyber Harassment is one in every of them. Social media provides a platform to speak with plenty, however it additionally becomes an area for negative comments and cyber harassment. Cyber Harassment is bullying dead through digital equipment’s like laptop computer and mobile devices. Cyber Harassment happens on social media, chat rooms and play platform wherever folks will read and share their own content. Cyber Harassment embrace causing hurtful messages posting sensitive photos or video on social media, and increasing mean rumors on-line. Cyber Harassment is large drawback. Thus, on forestall such drawback Cyber Harassment detection technique supported deep neural network is developed referred to as Convolution Neural Network (CNN). CNN is employed for the higher outcomes once contrasted with the present systems.

The Detection of Sexual Harassment and Chat Predators Using Artificial Neural Network

The vast increase in using social media sites like Twitter and Facebook led to frequent sexual_harassment on the Internet, which is considered a major societal problem. This paper aims to detect sexual_harassment and cyber_predators in early phase. We used deeplearning like Bidirectionally-long-short-term memory. Word representations are carefully reviewed in text specific to mapping to real number vectors. The chat sexual predators Detection_approach with the proposed_model. The best results obtained by the performance measured with F0.5-score were the result is_0.927 with proposed_models. The accuracy measured is_97.27% in the proposed_model. The comments sexual_harassment Detection_approach the result is_0.925 F0.5-score, and accuracy measured is_99.12%.

Data Mining for Cyberbullying and Harassment Detection in Arabic Texts

Broadly cyberbullying is viewed as a severe social danger that influences many individuals around the globe, particularly young people and teenagers. The Arabic world has embraced technology and continues using it in different ways to communicate inside social media platforms. However, the Arabic text has drawbacks for its complexity, challenges, and scarcity of its resources. This paper investigates several questions related to the content of how to protect an Arabic text from cyberbullying/harassment through the information posted on Twitter. To answer this question, we collected the Arab corpus covering the topics with specific words, which will explain in detail. We devised experiments in which we investigated several learning approaches. Our results suggest that deep learning models like LSTM achieve better performance compared to other traditional cyberbullying classifiers with an accuracy of 72%.

Hybrid ensemble approaches to online harassment detection in highly imbalanced data

Online harassment is a major threat to users of social media platforms, especially young adults and women. It can cause mental illnesses and impacts deeply and negatively economic institutions experiencing cyberbully attacks by losing their credibility and business. This makes automatic detection of online harassment extremely important. Most of current studies within this context apply machine-learning algorithms that assume balanced class distribution. However, this assumption does not hold for most real datasets. This research provides a comprehensive investigation of various approaches that combine diverse techniques under three dimensions: feature representation, imbalanced data handling, and supervised learning. For the first dimension, three word-embedding models have been considered, namely: word2vec, Glove, and SSWE. For the other two dimensions, nine techniques for balancing skewed class distributions have been employed to feed several learning models. In particular, resampling methods, cost-sensitive learning, and Weight-Selection strategy-based methods have been used with deep neural networks. The ultimate goal of this study is to evaluate the potential of using such hybrid approaches to handle the online harassment detection task efficiently using highly-imbalanced Twitter data and to select the best combination concerning the intended purpose. An extensive comparative study has been conducted, and the results have been discussed in terms of three evaluation metrics widely used for imbalanced classification. As main findings, Glove has been found as the best feature representation and some combinations as the best performing most notably LSTM and BLSTM with cost-sensitive learning and VL strategy.

WITHDRAWN: Recognition and prevention of cyberharassment in social media using classification algorithms

Cyberbullying is the aggressive pressure or harassment produce through electronic devices like laptops, desktop or computer, smart phones, and tablets. The harassment can occur in the environment of social networking sites, chat rooms, and gaming platforms where people can view and take part in the exchanging of their thoughts. The different kind of cyberbullying or harassment creates the humiliation through hateful comments on digital platforms/apps, or through SMS or messaging. It involves the sending posting, or sharing negative, disgusting, nasty or wrong information about another individual for causing embarrassment and the malicious and unjustified harming of a person's good reputation. Machine learning algorithms give a chance to find successfully predicting, detecting and preventing undesirable, harmful and unwanted opinions of human behavior, such as cyberharassment. It gives the pivotal information on new ways in the way of detecting and preventing antagonistic or violent human behavior, including cyber harassment detection and prevention in online social environmental sites. ML algorithms gives an prospect to completely expect, detect and prevent hurtful attitude of human behavior, such as cyber bullying. A model is proposed to detect and prevent the negative opinion in social media using Classification model algorithms. Profanity detection library function and Natural Language Processing are used to detect and prevent the negative opinions in the social media. This proposed model is helpful for the Government to identify the negative opinions given by the users in the social media and they can take actions. The development of such an application has the ability to stop and reduce the rate of cyberbullying that has been happening social media.

Learning to combine classifiers outputs with the transformer for text classification

Text classification is a fairly explored task that has allowed dealing with a considerable amount of problems. However, one of its main difficulties is to conduct a learning process in data with class imbalance, i.e., datasets with only a few examples in some classes, which often represent the most interesting cases for the task. In this context, text classifiers overfit some particular classes, showing poor performance. To address this problem, we propose a scheme that combines the outputs of different classifiers, coding them in the encoder of a transformer. Feeding also a BERT encoding of each example, the encoder learns a joint representation of the text and the outputs of the classifiers. These encodings are used to train a new text classifier. Since the transformer is a highly complex model, we introduce a data augmentation technique, which allows the representation learning task to be driven without over-fitting the encoding to a particular class. The data augmentation technique also allows for producing a balanced dataset. The combination of both methods, representation learning, and data augmentation, allows improving the performance of trained classifiers. Results in benchmark data for two text classification tasks (stance classification and online harassment detection) show that the proposed scheme outperforms all of its direct competitors.