Análisis comparativo sobre modelos de redes neuronales profundas para la detección de ciberbullying en redes sociales

Social media usage has been increased and it consists of both positive and negative effects. By considering the misusage of social media platforms by various cyberbullying methods like stalking, harassment there should be preventive methods to control these and to avoid mental stress. These extra words will expand the size of the vocabulary and influence the performance of the algorithm. Therefore, we come up with variant deep learning models like LSTM, BI-LSTM, RNN, BI-RNN, GRU, BI-GRU to detect cyberbullying in social media. These models are applied on Twitter, public comments data and performance were observed for these models and obtained improved accuracy of 90.4%.Introducción: el uso de las redes sociales se ha incrementado y tiene efectos tanto positivos como negativos. Al considerar el uso indebido de las plataformas de redes sociales a través de varios métodos de acoso cibernético, como el acecho y el acoso, debe haber métodos preventivos para controlarlos y evitar el estrés mental.Problema: estas palabras adicionales ampliarán el tamaño del vocabulario e influirán en el rendimiento del algoritmo.Objetivo: Detectar el ciberacoso en las redes sociales.Metodología: en este documento, presentamos variantes de modelos de aprendizaje profundo como la memoria a largo plazo (LSTM), memoria bidireccional a largo plazo (BI-LSTM), redes neuronales recurrentes (RNN), redes neuronales recurrentes bidireccionales (BI-RNN), unidad recurrente cerrada (GRU) y unidad recurrente cerrada bidireccional (BI-GRU) para detectar el ciberacoso en las redes sociales.Resultados: El mecanismo propuesto ha sido realizado, analizado e implementado sobre datos de Twitter con Accuracy, Precision, Recall y F-Score como medidas. Los modelos de aprendizaje profundo como LSTM, BI-LSTM, RNN, BI-RNN, GRU y BI-GRU se aplican en Twitter a los datos de comentarios públicos y se observó el rendimiento de estos modelos, obteniendo una precisión mejorada del 90,4 %.Conclusiones: Los resultados indican que el mecanismo propuesto es eficiente en comparación con los es-quemas del estado del arte.Originalidad: la aplicación de modelos de aprendizaje profundo para realizar un análisis comparativo de los datos de las redes sociales es el primer enfoque para detectar el ciberacoso.Restricciones: estos modelos se aplican solo en comentarios de datos textuales. El trabajo propio no se ha concentrado en datos multimedia como audio, video e imágenes

Current Limitations in Cyberbullying Detection: on Evaluation Criteria, Reproducibility, and Data Scarcity

The detection of online cyberbullying has seen an increase in societal importance, popularity in research, and available open data. Nevertheless, while computational power and affordability of resources continue to increase, the access restrictions on high-quality data limit the applicability of state-of-the-art techniques. Consequently, much of the recent research uses small, heterogeneous datasets, without a thorough evaluation of applicability. In this paper, we further illustrate these issues, as we (i) evaluate many publicly available resources for this task and demonstrate difficulties with data collection. These predominantly yield small datasets that fail to capture the required complex social dynamics and impede direct comparison of progress. We (ii) conduct an extensive set of experiments that indicate a general lack of cross-domain generalization of classifiers trained on these sources, and openly provide this framework to replicate and extend our evaluation criteria. Finally, we (iii) present an effective crowdsourcing method: simulating real-life bullying scenarios in a lab setting generates plausible data that can be effectively used to enrich real data. This largely circumvents the restrictions on data that can be collected, and increases classifier performance. We believe these contributions can aid in improving the empirical practices of future research in the field

The Use of a Large Language Model for Cyberbullying Detection

The dominance of social media has added to the channels of bullying for perpetrators. Unfortunately, cyberbullying (CB) is the most prevalent phenomenon in today’s cyber world, and is a severe threat to the mental and physical health of citizens. This opens the need to develop a robust system to prevent bullying content from online forums, blogs, and social media platforms to manage the impact in our society. Several machine learning (ML) algorithms have been proposed for this purpose. However, their performances are not consistent due to high class imbalance and generalisation issues. In recent years, large language models (LLMs) like BERT and RoBERTa have achieved state-of-the-art (SOTA) results in several natural language processing (NLP) tasks. Unfortunately, the LLMs have not been applied extensively for CB detection. In our paper, we explored the use of these models for cyberbullying (CB) detection. We have prepared a new dataset (D2) from existing studies (Formspring and Twitter). Our experimental results for dataset D1 and D2 showed that RoBERTa outperformed other models

Detection of Hate-Speech Tweets Based on Deep Learning: A Review

Cybercrime, cyberbullying, and hate speech have all increased in conjunction with the use of the internet and social media. The scope of hate speech knows no bounds or organizational or individual boundaries. This disorder affects many people in diverse ways. It can be harsh, offensive, or discriminating depending on the target's gender, race, political opinions, religious intolerance, nationality, human color, disability, ethnicity, sexual orientation, or status as an immigrant. Authorities and academics are investigating new methods for identifying hate speech on social media platforms like Facebook and Twitter. This study adds to the ongoing discussion about creating safer digital spaces while balancing limiting hate speech and protecting freedom of speech.   Partnerships between researchers, platform developers, and communities are crucial in creating efficient and ethical content moderation systems on Twitter and other social media sites. For this reason, multiple methodologies, models, and algorithms are employed. This study presents a thorough analysis of hate speech in numerous research publications. Each article has been thoroughly examined, including evaluating the algorithms or methodologies used, databases, classification techniques, and the findings achieved.   In addition, comprehensive discussions were held on all the examined papers, explicitly focusing on consuming deep learning techniques to detect hate speech

A review on deep-learning-based cyberbullying detection

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 different deep-learning-based models and frameworks. We have critically analyzed the existing DL-based cyberbullying detection techniques and identified their significant contributions and the future research directions they have presented. We have also summarized the datasets being used, including the DL architecture being used and the tasks that are accomplished for each dataset. Finally, several challenges faced by the existing researchers and the open issues to be addressed in the future have been presented

Artificial Intelligence and Machine Learning in Cybersecurity: Applications, Challenges, and Opportunities for MIS Academics

The availability of massive amounts of data, fast computers, and superior machine learning (ML) algorithms has spurred interest in artificial intelligence (AI). It is no surprise, then, that we observe an increase in the application of AI in cybersecurity. Our survey of AI applications in cybersecurity shows most of the present applications are in the areas of malware identification and classification, intrusion detection, and cybercrime prevention. We should, however, be aware that AI-enabled cybersecurity is not without its drawbacks. Challenges to AI solutions include a shortage of good quality data to train machine learning models, the potential for exploits via adversarial AI/ML, and limited human expertise in AI. However, the rewards in terms of increased accuracy of cyberattack predictions, faster response to cyberattacks, and improved cybersecurity make it worthwhile to overcome these challenges. We present a summary of the current research on the application of AI and ML to improve cybersecurity, challenges that need to be overcome, and research opportunities for academics in management information systems