Cyberbullying detection

Irish Interdisciplinary Journal of Science & Research, 2022

Social networking site is being hastily expanded in latest years, which provides platform to join human beings all over the global and share their hobbies. however, Social Networking sites is providing possibilities for cyberbullying activities. Cyberbullying is harassing or insulting a person through sending messages of hurting or threatening nature the usage of digital communication. Cyberbullying poses full-size chance to bodily and intellectual fitness of the victims. as a result, it's far crucial to reveal person's posts and clear out the detest speech related publish earlier than it's far spread. Detection of cyberbullying and the supply of next preventive measures are the main courses of movement to fight cyberbullying. This paper proposed a system for computerized detection and prevention cyberbullying thinking about the primary traits of cyberbullying such as intention to damage an character, repeatedly and over time and the use of abusive curl language or hate speech the use of LSTM set of rules. The proposed version is capable to stumble on hate content on Twitter robotically. This method is based on a bag of phrases and TFIDF (term frequency-inverse record frequency) technique. Those capabilities are used to educate deep mastering classifiers. The detest stage encapsulates the level of hate in a given virtual surroundings. We present methods to robotically decide the dislike stage, and block submit and users of the post with the aid of the utilising transfer mastering on pre-trained language fashions with annotated information to create automatic hate detectors.

2021

Online social networks (OSNs) play an integral role in facilitating social interaction; however, these social networks increase antisocial behavior, such as cyberbullying, hate speech, and trolling. Aggression or hate speech that takes place through short message service (SMS) or the Internet (e.g., in social media platforms) is known as cyberbullying. Therefore, automatic detection utilizing natural language processing (NLP) is a necessary first step that helps prevent cyberbullying. This research proposes an automatic cyberbullying method to detect aggressive behavior using a consolidated deep learning model. This technique utilizes multichannel deep learning based on three models, namely, the bidirectional gated recurrent unit (BiGRU), transformer block, and convolutional neural network (CNN), to classify Twitter comments into two categories: aggressive and not aggressive. Three well-known hate speech datasets were combined to evaluate the performance of the proposed method. The ...

Future Internet

Bullying is described as an undesirable behavior by others that harms an individual physically, mentally, or socially. Cyberbullying is a virtual form (e.g., textual or image) of bullying or harassment, also known as online bullying. Cyberbullying detection is a pressing need in today’s world, as the prevalence of cyberbullying is continually growing, resulting in mental health issues. Conventional machine learning models were previously used to identify cyberbullying. However, current research demonstrates that deep learning surpasses traditional machine learning algorithms in identifying cyberbullying for several reasons, including handling extensive data, efficiently classifying text and images, extracting features automatically through hidden layers, and many others. This paper reviews the existing surveys and identifies the gaps in those studies. We also present a deep-learning-based defense ecosystem for cyberbullying detection, including data representation techniques and dif...

Zenodo (CERN European Organization for Nuclear Research), 2022

The use of information and technology to bully a person online is referred to as cyberbullying. Individuals use Information and Communication Technology (ICT) to ridicule, embarrass, taunt, defame, intimidate, and criticise a person without making a direct eye contact. With the rise of social media, bullies have created a "virtual playground" in Facebook, Instagram, WhatsApp, Twitter and YouTube by targeting specific set of individuals or groups. It is necessary to deploy models and mechanisms in place for bullying contents, where the content is automatically detected and resolved, as the repercussions could have societal ramifications. This paper proposes a unique hybrid model for detecting cyberbullying in three different types of social media content such as textual, visual, and infographic data.

International Journal of Advances in Scientific Research and Engineering (ijasre), 2024

Cyberbullying has emerged as a significant concern in contemporary times, particularly due to its severe consequences, especially for children. In this paper, we propose an innovative machine learning-based approach aimed at accurately detecting cyberbullying messages and mitigating their harmful effects. The primary objectives of our research were twofold: developing a model capable of precisely identifying cyberbullying messages while distinguishing them from regular messages. To achieve this, we utilized a dataset of social media messages, labeled as normal, offensive, or hate messages. We adapted this dataset for binary classification, differentiating between cyberbullying and non-bullying messages. Our approach involved two distinct methods: firstly, utilizing Term Frequency-Inverse Document Frequency (TF-IDF) for traditional machine learning algorithms, and secondly, embedding texts for deep learning algorithms. We employed a total of 15 classifiers and performes a comprehensive comparison. The most successful algorithms from the first method were combined into a voting classifier, which demonstrated the highest accuracy of 96.5% during testing. Additionally, we assessed the impact of Recursive Feature Elimination with Cross-Validation (RFECV) on the model's performance and compared it with our baseline approach. Although the results exhibited slight fluctuations, the voting classifier consistently outperformed others with 96.6% accuracy. Our findings underline the effectiveness of the voting classifier based on machine learning algorithms, which delivered the most promising results. This approach holds the potential to be implemented in social media platforms or chat applications, serving as a valuable tool in the ongoing efforts to combat cyberbullying.

Opinion mining or sentiment analysis is considered as an important application of NLP (Natural Language Processing). Opinion mining is extracting the views that people express online. Those Websites which permits social interaction and collaboration can be considered as social media site, including networking sites such as Facebook, MySpace, and Twitter. Such sites offer today's youth a platform for amusement, entertainment, thrill, correspondence and communication with friends and furthermore have developed radically and exponentially as of late. This is the reason, there are various side effects, as cyberbullying has emerged as a serious issue afflicting children, adolescents and young adults. Machine learning techniques have conceivable ability to make automatic detection of bullying messages in social media, and this could develop a healthy and comparatively safe social media environment. Social media getting more and more popular in our day today life. By the popularity of the social media affects the people who involving into it. This makes the technology to work or to feel smarter and makes us lazier. On resulting to this robust and discriminative numerical representation learning of text messages is a critical issue. Hence here we propose a learning method to tackle this issue which is named as Semantic Enhanced Marginalized Denoising Auto Encoder (smsda).

Lecture Notes in Computer Science, 2018

Harassment by cyberbullies is a significant phenomenon on the social media. Existing works for cyberbullying detection have at least one of the following three bottlenecks. First, they target only one particular social media platform (SMP). Second, they address just one topic of cyberbullying. Third, they rely on carefully handcrafted features of the data. We show that deep learning based models can overcome all three bottlenecks. Knowledge learned by these models on one dataset can be transferred to other datasets. We performed extensive experiments using three real-world datasets: Formspring (˜12k posts), Twitter (˜16k posts), and Wikipedia(˜100k posts). Our experiments provide several useful insights about cyberbullying detection. To the best of our knowledge, this is the first work that systematically analyzes cyberbullying detection on various topics across multiple SMPs using deep learning based models and transfer learning.

15th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2023), 2025

Cyberbullying is a pressing issue in the digital age, especially in non-English-speaking communities. This study focuses on using deep learning techniques to detect cyberbullying in the Bangla language. The research employs a Bangla dataset that includes both cyberbullying and noncyberbullying text, which undergoes preprocessing, tokenization, and sequence transformation for input into a LSTM recurrent neural network. The LSTM model incorporates word embedding and dropout techniques to enhance its performance. Evaluation metrics like accuracy and AUC-ROC score are employed to assess the model's effectiveness. The study investigates many methods, such as vernacular-specific preparation and data augmenting, that are specifically designed for the identification of cyberbullying in the Bangla language. The findings demonstrate deep learning's capacity to identify cyberbullying in Bangla, which makes a significant contribution to resolving this problem in non-English settings. The research reveals impressive accuracy rates, with LSTM achieving 99.99% accuracy, and a different approach, Convolutional Neural Network (CNN), yielding 99.022%. Ensemble models, including Gradient Boosting, XGBoost, and Random Forest, are also employed and optimized using hyperparameter tuning. Notably, LSTM stands out as the most reliable classifier for cyberbullying detection, as confirmed by the experimental findings and recent research.

2018

Cyberbullying and cyberaggression are serious and widespread issues increasingly affecting Internet users. With the widespread of social media networks, bullying, once limited to particular places, can now occur anytime and anywhere. Cyberaggression refers to aggressive online behaviour that aims at harming other individuals, and involves rude, insulting, offensive, teasing or demoralising comments through online social media. Considering the dangerous consequences that cyberaggression has on its victims and its rapid spread amongst internet users (specially kids and teens), it is crucial to understand how cyberbullying occurs to prevent it from escalating. Given the massive information overload on the Web, there is an imperious need to develop intelligent techniques to automatically detect harmful content, which would allow the large-scale social media monitoring and early detection of undesired situations. This paper presents the Isistanitos’s approach for detecting aggressive con...

IEEE Access, 2022

Cyberbullying (CB) has become increasingly prevalent in social media platforms. With the popularity and widespread use of social media by individuals of all ages, it is vital to make social media platforms safer from cyberbullying. This paper presents a hybrid deep learning model, called DEA-RNN, to detect CB on Twitter social media network. The proposed DEA-RNN model combines Elman type Recurrent Neural Networks (RNN) with an optimized Dolphin Echolocation Algorithm (DEA) for finetuning the Elman RNN's parameters and reducing training time. We evaluated DEA-RNN thoroughly utilizing a dataset of 10000 tweets and compared its performance to those of state-of-the-art algorithms such as Bi-directional long short term memory (Bi-LSTM), RNN, SVM, Multinomial Naive Bayes (MNB), Random Forests (RF). The experimental results show that DEA-RNN was found to be superior in all the scenarios. It outperformed the considered existing approaches in detecting CB on Twitter platform. DEA-RNN was more efficient in scenario 3, where it has achieved an average of 90.45% accuracy, 89.52% precision, 88.98% recall, 89.25% F1-score, and 90.94% specificity.