## 1 Introduction  
The rapid proliferation of information in the digital age has transformed the way individuals consume news and interact with content. While access to diverse perspectives can enhance public discourse, it has also given rise to a troubling surge in misinformation and disinformation. Misinformation, defined as false or misleading information spread without intent to deceive, and disinformation, which is deliberately misleading, pose significant threats to informed decision-making and democratic processes. The consequences of these phenomena are far-reaching, affecting public health, political stability, and societal cohesion [1]. As online platforms become the primary source of news for many, the challenge of distinguishing credible information from false narratives has intensified [2]. Traditional fact-checking methods often struggle to keep pace with the sheer volume and speed of modern information dissemination. Consequently, this has created a pressing need for automated, scalable solutions that can identify and mitigate the spread of false information efficiently. Problem Statement  
Despite advances in artificial intelligence (AI), accurately detecting fake news remains a significant challenge. Current approaches often fail to generalize across diverse linguistic contexts and topics, and the trade-offs between model accuracy, adaptability, and resource efficiency are not well understood. Additionally, the high computational costs of fine-tuning large language models (LLMs) present barriers to their widespread adoption. Addressing these challenges is critical for building reliable tools that enhance information integrity in the news ecosystem. Research Goals and Significance  
In this study, these challenges are addressed by investigating the performance of state-of-the-art deep neural network (DNN) models, including convolutional neural networks (CNNs), bidirectional encoder representations from transformers (BERT), and generative pre-trained transformers (GPT), in classifying fake news. The research aims to achieve the following:  
1. Evaluate the effectiveness of advanced AI models in fake news detection, focusing on large language models (LLMs). 2. Compare the performance of these models before and after fine-tuning using few-shot learning techniques. 3. Examine the costs and trade-offs associated with fine-tuning LLMs and their implications for real-world applications. 4. Explore the transformative potential of LLMs to provide automated detection and actionable insights for the news industry. 5. Investigate the following critical questions that have yet to be thoroughly examined in previous studies: * Q1: are traditional NLP and CNN models or LLMs more accurate in fake news detection tasks? * Q2: among the GPT-4 Omni family, which model performs best prior to fine-tuning? * Q3: after fine-tuning with few-shot learning, which model in the GPT-4 Omni family demonstrates superior performance? * Q4: what is the significance of the costs associated with fine-tuning LLMs, and how do these costs impact performance in the news sector? * Q5: how can LLMs be effectively leveraged to assess fake news, and what transformative effects can they have on the news industry through automated detection and actionable insights? By addressing these objectives, in this paper, we seek to contribute to the development of practical, robust tools for combating misinformation. The findings aim to empower journalists, policymakers, and the public with effective strategies and technologies to navigate an increasingly complex media landscape. The structure of this paper is as follows:* Section 2 presents a comprehensive review of the existing literature on fake news detection and classification, with a focus on the application of DNN models in the news sector. * Section 3 outlines the methodologies employed in this study, detailing the fine-tuning of models using few-shot learning techniques to ensure transparency and reproducibility.