V era CT Scan: Retrieval-Augmented Fake News Detection with Justifiable Reasoning (2024)

Abstract: The proliferation of fake news poses a significant threat not only by disseminating misleading information but also by undermining the very foundations of democracy. The recent advance of generative artificial intelligence has further exacerbated the challenge of distinguishing genuine news from fabricated stories. In response to this challenge, we introduce VeraCT Scan, a novel retrieval-augmented system for fake news detection. This system operates by extracting the core facts from a given piece of news and subsequently conducting an internet-wide search to identify corroborating or conflicting reports. Then sources’ credibility is leveraged for information verification. Besides determining the veracity of news, we also provide transparent evidence and reasoning to support its conclusions, resulting in the interpretability and trust in the results. In addition to GPT-4 Turbo, Llama-2 13B is also fine-tuned for news content understanding, information verification, and reasoning. Both implementations have demonstrated state-of-the-art accuracy in the realm of fake news detection.

## introduction the contemporary digital landscape is rife with the proliferation of fake news, presenting a multifaceted challenge that undermines public discourse, affects democratic processes, and incites real-world consequences @xcite . fake news, characterized by the deliberate dissemination of misinformation, exploits the rapid spread of information online, often outpacing the verification processes that traditional media outlets adhere to. fake news detection is defined as the process of identifying and verifying the veracity of news content, employing various computational and manual methods. this process involves distinguishing between true and false information, considering the intent behind the information dissemination, whether it be to mislead, harm, or manipulate public opinion. traditional approaches in fake news detection have primarily focused on the linguistic features, also called content-based detection @xcite @xcite @xcite @xcite , which demands laborious feature engineering and is ineffective when the fake news is written by imitating the real news to mislead intentionally. another line of research is the social contextbased method @xcite @xcite @xcite , which analyzes the interactions among users, publishers, and posts. however, the feasibility of obtaining user information is challenging for the real-world application. a more recent research approach is the knowledge-based method @xcite @xcite @xcite @xcite , which discerns the veracity of a factual claim by comparing against the evidence retrieved from external knowledge base. however, current approaches often do not fully utilize external resources like the internet. additionally, there is a lack of development and optimization of a comprehensive end-to-end pipeline that includes news comprehension, search optimization, verification, and reasoning. in this paper, we introduce veract scan, a novel retrieval-augmented system for fake news detection. veract scan initiates this process by identifying key factual claims across multiple levels of granularity. for each identified factual claim, a comprehensive internet search is conducted to gather relevant information. then, the veracity of the news is determined by combining this typically disparate and conflicting information, taking into account the varying degrees of source credibility. to increase the trustworthiness of our approach, we underscore the necessity of a transparent reasoning process and provide rationales for each supporting or conflicting judgment. in summary, our main contributions are: (i) we introduce veract scan, that operates across multiple levels of information granularity, employing optimized information retrieval techniques to enhance fake news detection performance. (ii) we investigate the generation of verification rationales as a means to increase the system's transparency and trustworthiness. additionally, we address the management of conflicting evidence by leveraging the credibility of sources, thereby improving the reliability of the verification process. (iii) we conduct a comprehensive evaluation of veract scan using several fake news detection datasets. our results demonstrate that the system achieves state-of-the-art performance in news verification tasks, employing both prompted and fine-tuned llms. ## related work in this section, we first review the progress of fake news detection and then discuss the retrievalaugmented generation methods. ## approach in this paper, the term "claim" refers to the fact stated in a news article. the terms "factual claim extraction" and "fact extraction" are used interchangeably throughout the paper. figure 1 shows the main workflow of veract scan. we prompt gpt-4 turbo for key fact extraction, query generation, verification, and rationale generation (see appendix a for prompts being used). these individual components can be easily exchange to other llms or search engines. in this work, the outputs from gpt-4 turbo, supplemented with manual reviews, serve as training data to fine-tune llama-2 13b @xcite , enabling it to support these tasks as well. regarding the search component, we employ both google and our proprietary in-house news search engine for comprehensive information retrieval. ## experimental settings in this section, we conduct comprehensive fake news detection benchmarks using multiple datasets. ## experimental results the performance with the buzzfeednews dataset is detailed in in ## conclusion and future work in this paper, we present veract scan, a novel retrieval-augmented system for fake news detection. two of our implementations, properly prompted gpt-4 turbo and fine-tuned llama-2 13b demonstrated notable accuracy in detection. specifically, the gpt-4 turbo implementation exhibited stateof-the-art performance in several datasets. veract scan is especially successful in identifying the latest instances of fake news. this emphasizes the critical role of search result relevance in gathering compelling evidence. our observations reveal that the rationales generated by llms offer rich insights into potentially dubious aspects with a high degree of details. as a future work, we plan to investigate the potential of using these rationales as input features for the final verification classifier. and throughout our evaluations, llama-2 13b consistently lags behind gpt-4 turbo in terms of detection accuracy. we will explore more effective fine-tuning strategies to narrow this performance gap. furthermore, we observe that within the entire system, the majority of errors occur during the verification stage, with a smaller fraction arising during the claim extraction phase. the causes of these errors include: (i) irrelevant search results used for verification. (ii) updated news events leading to outdated reports being used for verification. (iii) each report only supporting a part of the claim, necessitating the proper merging of relevant information from multiple news reports for full verification. (iv) improper normalization of named entities or temporal expressions during the claim extraction stage, making alignment difficult during verification (e.g., "last weekend" vs. an exact date). we hope to address these issues in future work.