FAKE PROFILE IDENTIFICATION IN TWITTER USING MACHINE LEARNING

Abstract

In the contemporary landscape of pervasive social
media usage, the identification of fake profiles stands
as a crucial element in preserving online security.
This research delves into the realm of machine
learning algorithms, focusing on the comparative
analysis of LightGBM and Support Vector Machine
(SVM) for the task of detecting fake accounts on
social media platforms. Our study harnesses the
power of these two algorithms, both known for their
robust capabilities, and evaluates their performance
against each other. The research utilizes a diverse set
of features derived from user profiles, posting
behavior, and linguistic patterns, with Natural
Language Processing (NLP) techniques applied to
extract nuanced insights from textual content.