Machine learning is used to detect instances of cyber bullying.

Introduction

In today’s digital age, cyber bullying has become a prevalent issue affecting individuals of all ages. With the rise of social media and online platforms, individuals are faced with the challenge of navigating through harmful online behaviors that can have serious consequences on mental health and well-being. As a student pursuing a Bachelor of Technology degree in India, it is essential to address this issue through the use of advanced technologies such as machine learning.

Problem Statement

Cyber bullying detection is a complex task that involves identifying and monitoring online behaviors that may be considered harmful or abusive. Traditional methods of detecting cyber bullying rely on manual intervention and reporting, which can be time-consuming and often ineffective. Therefore, there is a need for a more efficient and accurate system that can automatically detect and prevent cyber bullying in real-time.

Existing System

The existing system for cyber bullying detection relies heavily on human intervention and reporting. Users are required to report any instances of cyber bullying they encounter online, which can be unreliable and subject to bias. Furthermore, manual monitoring of online platforms is not scalable and can miss out on subtle forms of cyber bullying that may go unnoticed.

Disadvantages

The disadvantages of the existing system for cyber bullying detection are vast. Firstly, relying on manual reporting can lead to underreporting of cyber bullying incidents, as individuals may be hesitant to come forward. Additionally, manual monitoring is time-consuming and inefficient, resulting in delayed responses to cyber bullying incidents. Moreover, the lack of automation in the existing system makes it difficult to capture and analyze large volumes of data in real-time, leading to missed opportunities for intervention.

Proposed System

To address the shortcomings of the existing system, a proposed system for cyber bullying detection using machine learning can be implemented. By harnessing the power of artificial intelligence and data analytics, machine learning algorithms can be trained to identify patterns of cyber bullying behavior and automatically flag harmful content in real-time. This proactive approach to cyber bullying detection can help prevent incidents before they escalate and impact the well-being of individuals.

Conclusion

In conclusion, the current system for cyber bullying detection is flawed and inefficient, necessitating the need for technological advancements in the form of machine learning. As a student studying Bachelor of Technology in India, it is imperative to explore innovative solutions to address pressing societal issues such as cyber bullying. By implementing a machine learning-based system for cyber bullying detection, we can create a safer online environment for individuals to interact and communicate without fear of harassment or abuse.