Detecting Persecution on Interactive Networks Using Machine Learning Methods

Authors

  • Sheela B V
  • Rajesh N

Keywords:

Interactive networks, Internet use, Online safet, Persecution detection, Social media analysis

Abstract

Persecution on interactive networks, such as
social media platforms and online forums,
poses significant challenges to ensuring a safe
and inclusive digital environment. Detecting
and addressing instances of persecution is
crucial for safeguarding individuals' rights
and promoting online harmony. This research
paper presents a novel approach to detecting
persecution on interactive networks using
machine learning methods.
The proposed framework leverages the power
of machine learning algorithms to
automatically identify patterns and indicators
of persecution within user-generated content.
The process begins with the collection and
preprocessing of a large-scale dataset
comprising diverse interactions on interactive
networks. Various features, including textual,
contextual, and user-based attributes, are
extracted to capture the nuanced aspects of
persecution.
Next, a range of machine learning techniques,
such as natural language processing,
sentiment analysis, and social network
analysis, are employed to analyze the dataset.
Multiple classification models, such as
support vector machines, random forests, and
deep learning architectures, are trained and
evaluated to identify the most effective
approach for persecution detection.
The experimental results demonstrate the
effectiveness of the proposed methodology in
accurately detecting instances of persecution
on interactive networks. The trained models
achieve high precision and recall rates,
highlighting their ability to discern subtle
instances of persecution and distinguish them
from non-persecutory content. The findings of
this study contribute to the development of
automated tools that can aid in identifying
and mitigating persecution on interactive
networks, thereby fostering a safer and more
inclusive digital space.

Published

2023-07-27

Issue

Section

Articles