Textual Aggression Detection through Deep Learning

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...

Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)

In the past few years, bully and aggressive posts on social media have grown significantly, causing serious consequences for victims/users of all demographics. Majority of the work in this field has been done for English only. In this paper, we introduce a deep learning based classification system for Facebook posts and comments of Hindi-English Code-Mixed text to detect the aggressive behaviour of/towards users. Our work focuses on text from users majorly in the Indian Subcontinent. The dataset that we used for our models is provided by TRAC-1 1 in their shared task. Our classification model assigns each Facebook post/comment to one of the three predefined categories: "Overtly Aggressive", "Covertly Aggressive" and "Non-Aggressive". We experimented with 6 classification models and our CNN model on a 10 K-fold crossvalidation gave the best result with the prediction accuracy of 73.2%.

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 ...

IRJET, 2022

The use of social media is widespread and it is ordinary for society of variable ages to have an account on social media platforms. Misuse of social media profiles has become quite easy for the same reason.. Cyberbullying or bullying through social media websites have been an ongoing issue that has been raised with the creation of social media. Since it has adverse effects on people’s psychological behavior and mental health , timely detection and prevention of social media bullying is a very important factor in the world of the internet. Machine learning and Deep learning are the typical approaches adopted for cyberbully detection. In this research paper, we take a dataset containing tweets from the popular social media website Twitter to find texts that are possible cases of bullying. We create a hybrid bully detection system named Stacking Algorithm by merging three ML algorithms – K Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF) to detect cyberbully texts in an accurate manner. The texts are checked and classified into three categories – Not Bullying, Racism and Sexism. We also include a section of Convolutional Neural Network (CNN) which identifies the bully texts accurately. A GUI is designed to display the accuracy percentage of the hybrid system as well as CNN . The accuracy measures of the two systems are evaluated, and it is determined that CNN produces a more precise estimate than the Stacking Algorithm.

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.

Al-Nahrain Journal of Science, 2023

The number of users of social media sites has increased nowadays, and while these sites have many benefits, they also have many damages that have grown with the increasing number of users. Among these damages that have spread in social media sites in our time is the phenomenon of cyberbullying. It has become necessary to find solutions to detect it to prevent and hold bullies accountable to reduce the phenomenon of cyberbullying, which has great health and mental effects on the victim in society. There have been many attempts to build models to detect and classify cyberbullying by using machine learning and deep learning algorithms with different sets of data that were collected from social media sites such as Twitter, YouTube, Facebook, Instagram, and others. In this work, we show a group of previous studies that used machine learning and deep learning algorithms in good attempts to detect and classify the phenomenon of cyberbullying.

IRJET, 2021

Cyberbullying is a serious threat to both the short and long-term well-being of social media users. We study detection of cyberbullying in photosharing networks, with an eye on developing earlywarning mechanisms for the prediction of posted images vulnerable to attacks. Cyberbullying disturbs harassment online, with alarming implications. Addressing this problem in online environments demands the ability to automatically detect cyberbullying and to identify the roles that participants assume in social interactions. It exists in different ways, and is in textual format in most social networks. Cyberbullying is the use of technology as a medium to bully someone. Cyberbullying constitutes a threat to adolescents' psychosocial wellbeing that developed alongside technological progress. A system is proposed to give a double characterization of cyberbullying. Deep learning based models have discovered ways in the identification of digital harassing occurrences, asserting that they can beat the restrictions of the ordinary models, and improve the discovery execution. However, numerous oldschool models are accessible to control the incident, the need to successfully order the tormenting is as yet weak.

2018

Cyberbullying is a disturbing online misbehaviour with troubling consequences. It appears in different forms, and in most of the social networks, it is in textual format. Automatic detection of such incidents requires intelligent systems. Most of the existing studies have approached this problem with conventional machine learning models and the majority of the developed models in these studies are adaptable to a single social network at a time. In recent studies, deep learning based models have found their way in the detection of cyberbullying incidents, claiming that they can overcome the limitations of the conventional models, and improve the detection performance. In this paper, we investigate the findings of a recent literature in this regard. We successfully reproduced the findings of this literature and validated their findings using the same datasets, namely Wikipedia, Twitter, and Formspring, used by the authors. Then we expanded our work by applying the developed methods on...

Big Data Analytics and Knowledge Discovery

Cyberbullying is a disturbing online misbehaviour with troubling consequences. It appears in different forms, and in most of the social networks, it is in textual format. Automatic detection of such incidents requires intelligent systems. Most of the existing studies have approached this problem with conventional machine learning models and the majority of the developed models in these studies are adaptable to a single social network at a time. In recent studies, deep learning based models have found their way in the detection of cyberbullying incidents, claiming that they can overcome the limitations of the conventional models, and improve the detection performance. In this paper, we investigate the findings of a recent literature in this regard. We successfully reproduced the findings of this literature and validated their findings using the same datasets, namely Wikipedia, Twitter, and Formspring, used by the authors. Then we expanded our work by applying the developed methods on a new YouTube dataset (~54k posts by ~4k users) and investigated the performance of the models in new social media platforms. We also transferred and evaluated the performance of the models trained on one platform to another platform. Our findings show that the deep learning based models outperform the machine learning models previously applied to the same YouTube dataset. We believe that the deep learning based models can also benefit from integrating other sources of information and looking into the impact of profile information of the users in social networks.