Credibility signals represent a wide range of heuristics that are typically
used by journalists and fact-checkers to assess the veracity of online content.
Automating the task of credibility signal extraction, however, is very
challenging as it requires high-accuracy signal-specific extractors to be
trained, while there are currently no sufficiently large datasets annotated
with all credibility signals. This paper investigates whether large language
models (LLMs) can be prompted effectively with a set of 18 credibility signals
to produce weak labels for each signal. We then aggregate these potentially
noisy labels using weak supervision in order to predict content veracity. We
demonstrate that our approach, which combines zero-shot LLM credibility signal
labeling and weak supervision, outperforms state-of-the-art classifiers on two
misinformation datasets without using any ground-truth labels for training. We
also analyse the contribution of the individual credibility signals towards
predicting content veracity, which provides new valuable insights into their
role in misinformation detection.

本文研究大型语言模型（LLMs）是否可以有效激发一组 18 个可信度信号，以产生每个信号的弱标签，然后使用弱监督方法来预测内容的真实性，并演示了该方法在两个虚假信息数据集上的性能优于现有的分类器，同时分析了各个可信度信号对内容真实性预测的贡献，从而为虚假信息检测提供了新的有价值的见解。

使用 LLM 预测的可信度信号和弱监督检测虚假信息

Detecting Misinformation with LLM-Predicted Credibility Signals and Weak  Supervision

Users' involvement in creating and propagating news is a vital aspect of fake
news detection in online social networks. Intuitively, credible users are more
likely to share trustworthy news, while untrusted users have a higher
probability of spreading untrustworthy news. In this paper, we construct a
dual-layer graph (i.e., the news layer and the user layer) to extract multiple
relations of news and users in social networks to derive rich information for
detecting fake news. Based on the dual-layer graph, we propose a fake news
detection model named Us-DeFake. It learns the propagation features of news in
the news layer and the interaction features of users in the user layer. Through
the inter-layer in the graph, Us-DeFake fuses the user signals that contain
credibility information into the news features, to provide distinctive
user-aware embeddings of news for fake news detection. The training process
conducts on multiple dual-layer subgraphs obtained by a graph sampler to scale
Us-DeFake in large scale social networks. Extensive experiments on real-world
datasets illustrate the superiority of Us-DeFake which outperforms all
baselines, and the users' credibility signals learned by interaction relation
can notably improve the performance of our model.

本文介绍了一种基于双层图模型的假新闻检测模型 Us-DeFake，通过提取社交网络中新闻和用户之间的多重关系以及学习用户可信度信息以提高模型性能，实验结果表明该模型优于所有基准模型。