利用 ChatGPT 增强事实核查重新审视假新闻数据集
A Revisit of Fake News Dataset with Augmented Fact-checking by ChatGPT
AI总结：
本文用 ChatGPT 对人工记者验证的假新闻数据集进行增强事实核查，构建 ChatGPT-FC，并定量比较人类与 LLM 在多维度可信度评估上的异同，表明 LLM 可用于初步筛查并缓解人工偏见。
摘要：
近年来，假新闻的泛滥已成为一个关键问题，需要投入大量精力进行检测。然而，现有的假新闻检测数据集来源于人类记者，由于该任务具有高度主观性，这些数据集可能存在固有的偏见局限。在本文中，我们利用大型语言模型（ChatGPT）进行增强事实核查，重新审视由人类记者验证的现有假新闻数据集，并将增强后的假新闻数据集命名为 ChatGPT-FC。我们定量分析了人类记者与 LLM 在评估新闻主体可信度、新闻创作者可信度、时间敏感性和政治框架方面的区别与相似之处。研究结果凸显了 LLM 作为初步筛选方法的潜力，为缓解人类记者的固有偏见并改进假新闻检测提供了一条有前景的途径。
英文摘要：
The proliferation of fake news has emerged as a critical issue in recent years, requiring significant efforts to detect it. However, the existing fake news detection datasets are sourced from human journalists, which are likely to have inherent bias limitations due to the highly subjective nature of this task. In this paper, we revisit the existing fake news dataset verified by human journalists with augmented fact-checking by large language models (ChatGPT), and we name the augmented fake news dataset ChatGPT-FC. We quantitatively analyze the distinctions and resemblances between human journalists and LLM in assessing news subject credibility, news creator credibility, time-sensitive, and political framing. Our findings highlight LLM's potential to serve as a preliminary screening method, offering a promising avenue to mitigate the inherent biases of human journalists and enhance fake news detection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
