Deceptive Content Detection Using Machine Learning

Abstract

Deceptive content, such as fake news, poses a significant challenge in today's information landscape, influencing public opinion and decision-making processes. This paper presents an innovative approach for the detection of deceptive content using machine learning techniques. The proposed system leverages a combination of natural language processing and supervised learning algorithms to identify patterns indicative of misinformation in textual data Our approach leverages term frequency-inverse document frequency (TF-IDF) of bag-of-words and n-grams as feature extraction methods, complemented by the utilization of Support Vector Machine (SVM) as a classifier. Additionally, we introduce a meticulously curated dataset comprising both fake and genuine news articles to train and evaluate our proposed system. Our findings underscore the efficacy of the developed framework, demonstrating its capability in discerning between fake and authentic news articles. Keywords — Deceptive Content, TfidfVectorizer, Natural Language Processing, Text Classification, Passive-Aggressive Classifier, Machine Learning