This comprehensive survey serves as an indispensable resource for researchers
embarking on the journey of fake news detection. By highlighting the pivotal
role of dataset quality and diversity, it underscores the significance of these
elements in the effectiveness and robustness of detection models. The survey
meticulously outlines the key features of datasets, various labeling systems
employed, and prevalent biases that can impact model performance. Additionally,
it addresses critical ethical issues and best practices, offering a thorough
overview of the current state of available datasets. Our contribution to this
field is further enriched by the provision of GitHub repository, which
consolidates publicly accessible datasets into a single, user-friendly portal.
This repository is designed to facilitate and stimulate further research and
development efforts aimed at combating the pervasive issue of fake news.

这份综合调查是研究人员在进行假新闻检测之旅中不可或缺的资源，通过强调数据集质量和多样性的关键作用，它强调了这些元素对检测模型的有效性和稳健性的重要性。调查详细概述了数据集的关键特征、使用的各种标记系统和可能影响模型性能的普遍偏见。此外，它还涉及关键的伦理问题和最佳实践，为当前可用数据集的情况提供了全面的概述。我们为该领域的贡献进一步丰富，提供了 GitHub 存储库，将公开可访问的数据集汇集到一个单一且用户友好的门户中。这个存储库旨在促进和推动进一步的研究和开发工作，旨在解决普遍存在的假新闻问题。

假新闻检测：数据见真章！

Fake News Detection: It's All in the Data!

A number of labeling systems based on text have been proposed to help monitor
work on the United Nations (UN) Sustainable Development Goals (SDGs). Here, we
present a systematic comparison of systems using a variety of text sources and
show that systems differ considerably in their specificity (i.e., true-positive
rate) and sensitivity (i.e., true-negative rate), have systematic biases (e.g.,
are more sensitive to specific SDGs relative to others), and are susceptible to
the type and amount of text analyzed. We then show that an ensemble model that
pools labeling systems alleviates some of these limitations, exceeding the
labeling performance of all currently available systems. We conclude that
researchers and policymakers should care about the choice of labeling system
and that ensemble methods should be favored when drawing conclusions about the
absolute and relative prevalence of work on the SDGs based on automated
methods.

本研究比较了不同文本源使用的多个基于文本的系统以监测联合国可持续发展目标，证明了这些系统在真阳性率和真阴性率方面存在显著差异，偏好某些可持续发展目标，且受到分析的文本类型和数量的影响。研究发现汇集多个标注系统的集成模型能改善这些限制，以优于所有现有系统的标注性能。自动化方法应结合集成方法作为推断联合国可持续发展目标研究工作的使用。