Decoding the Language of Deception: A Textual Analysis of Fraud Trends in News Media with Advanced NLP Technique

Abstract

This research aims to unveil the extent and nature of deception in news media outlets, employing advanced natural language processing (NLP) techniques. NLP has reached an exciting point in its development, which has made it a key tool for analyzing large scale textual data across industries. As a result, fraud detection has become one of the most important use cases of NLP because it can reveal hidden patterns and linguistic indicators. The traditional analysis of fraud is usually based on financial and transactional data, but the analysis of text provides a new view to the way language affects the perception of trustworthiness and deceit. Through integrating various methodologies such as sentiment analysis, topic modeling, and named entity recognition, the investigation meticulously analyzes news articles to identify subtle linguistic indicators of fraudulent behavior. Utilizing specialized NLP software packages like VADER, spaCy, Gensim, and NLTK, the study effectively detects intricate patterns in the representation of fraud within media narratives and monitors shifts in public attitudes towards deceitful actions. The results reveal distinct linguistic patterns and trends in the portrayal of fraud, offering novel insights into the media’s role in shaping public perception of deception. These findings provide significant contributions to the theoretical framework of AI-driven news analysis and have practical implications for journalism and policymaking. This research not only sets new benchmarks in media monitoring and analysis by merging computational linguistics with fraud detection techniques but also plays a crucial role in enhancing the integrity of information dissemination and fostering a more accurately informed public sphere.