Combating Cyberbullying with Machine Learning and Deep Learning

EasyChair Preprint 13035

Abstract

Cyberbullying has become a prevalent issue in today's digital age, with severe consequences for individuals' mental health and well-being. Traditional methods for combating cyberbullying often fall short due to the sheer volume and complexity of online content. This abstract explores the potential of machine learning and deep learning techniques in addressing this societal problem.

Machine learning algorithms offer the ability to automatically analyze large amounts of online data, such as social media posts, messages, and comments, to identify instances of cyberbullying. These algorithms can be trained on labeled datasets, where human experts have annotated examples of cyberbullying, enabling them to learn patterns and characteristics associated with harmful behavior. By leveraging natural language processing and sentiment analysis techniques, machine learning models can accurately detect and classify instances of cyberbullying, distinguishing them from harmless online interactions.

Deep learning, a subset of machine learning, provides even greater potential in combating cyberbullying due to its capability to process complex and unstructured data. Deep neural networks, such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs), can capture intricate patterns and nuances in text, images, and videos, thus enabling more accurate identification of cyberbullying instances. These models can learn from vast amounts of data, continually improving their performance as they encounter novel forms of abusive content.

Keyphrases: Cyberbullying, deep learning, machine learning

@booklet{EasyChair:13035,
  author    = {Abil Robert},
  title     = {Combating Cyberbullying with Machine Learning and Deep Learning},
  howpublished = {EasyChair Preprint 13035},
  year      = {EasyChair, 2024}}