International Research Journal of Engineering and Technology (IRJET) Volume: 12 Issue: 02 | Feb 2025
www.irjet.net
e-ISSN: 2395-0056 p-ISSN: 2395-0072
Deception Detection Using a Passive-Aggressive Classifier: A Novel Approach to Identify Deceptive Communication 1Rehamat Bee, 2Diya N, 3 Prof. L Sreenivasa Perumal 1,2 UG student, Dept. of CSE-AIML, AMC Engineering College, KARNATAKA, INDIA.
3Assistant professor, Dept. of CSE-AIML, AMC Engineering College, KARNATAKA, INDIA
---------------------------------------------------------------------***---------------------------------------------------------------------We allow the system to understand the nuanced ABSTRACT - This study presents a passive-aggressive
differences between true and misleading news articles by training the passive-aggressive classifier on labelled datasets that comprise both types of articles. This classifier's capacity to adjust to new information without requiring a lot of retraining is one of its most notable properties, which makes it a reliable answer for the constantly changing world of internet disinformation.
classifier for detecting fake news, leveraging its adaptability for real-time misinformation detection. Trained on labeled datasets, it identifies deceptive patterns efficiently without full retraining. Results show its effectiveness in curbing misinformation and enhancing trust in digital communication. Keywords: Fake news detection, Passiveaggressive classifier, Machine learning, Text classification, Misinformation detection.
2. PROBLEM STATEMENT:
1. INTRODUCTION
The rapid spread of misleading and deceptive information, particularly through digital platforms, poses a significant challenge to individuals and society. Traditional misinformation detection methods often require extensive retraining and struggle to adapt to evolving deceptive strategies. There is a need for an efficient and adaptive approach to identify deceptive communication in real time. This study addresses the problem by utilizing a passive-aggressive classifier, a machine learning model capable of handling largescale text classification tasks while dynamically adjusting to new data. The proposed solution aims to enhance the accuracy and efficiency of deception detection, thereby improving trust and reliability in digital communication.
The way we consume information has changed significantly in the fast-paced digital world of today. Social network 29. For millions of people, platforms, news websites, and online discussion boards are becoming their main news sources. Although there are advantages to this accessibility, there has also been a concerning increase in False or misleading material that is passed off as real news with the intention of misleading or controlling readers is referred to as fake news. This phenomenon has the potential to have detrimental effects on public opinion, cause fear in times of crisis, and undermine confidence in reliable information sources. The challenge of identifying fake news is compounded by the sheer volume of content generated every day. Traditional methods of fact-checking, which often rely on manual verification, are not only time-consuming but also impractical in the face of such overwhelming data. Automated solutions that can effectively and precisely identify misleading content in real time are therefore desperately needed.
3. METHODOLOGY Outline the methodology employed in our project, "Deception Detection Using a Passive-Aggressive Classifier." Our approach is structured around three main components: the passive-aggressive classifier, data collection and pre-processing, and model training and evaluation. Each of these components ensuring the effectiveness and reliability of our fake news detection system.
This project, titled "Deception Detection Using a Passive-Aggressive Classifier," by leveraging machine learning techniques. At the heart of our approach is the Passive-Aggressive Classifier, a powerful algorithm designed for text classification tasks. This model effectively distinguishes between authentic and fraudulent news stories because it is especially wellsuited for finding patterns in textual data.
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A. Passive-Aggressive Classifier: A potent linear model created especially for extensive text classification tasks is the PassiveAggressive Classifier. Its distinct learning strategy,
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