Diaspora communities are disproportionately impacted by off-the-radar
misinformation and often neglected by mainstream fact-checking efforts,
creating a critical need to scale-up efforts of nascent fact-checking
initiatives. In this paper we present SynDy, a framework for Synthetic Dynamic
Dataset Generation to leverage the capabilities of the largest frontier Large
Language Models (LLMs) to train local, specialized language models. To the best
of our knowledge, SynDy is the first paper utilizing LLMs to create
fine-grained synthetic labels for tasks of direct relevance to misinformation
mitigation, namely Claim Matching, Topical Clustering, and Claim Relationship
Classification. SynDy utilizes LLMs and social media queries to automatically
generate distantly-supervised, topically-focused datasets with synthetic labels
on these three tasks, providing essential tools to scale up human-led
fact-checking at a fraction of the cost of human-annotated data. Training on
SynDy's generated labels shows improvement over a standard baseline and is not
significantly worse compared to training on human labels (which may be
infeasible to acquire). SynDy is being integrated into Meedan's chatbot
tiplines that are used by over 50 organizations, serve over 230K users
annually, and automatically distribute human-written fact-checks via messaging
apps such as WhatsApp. SynDy will also be integrated into our deployed
Co-Insights toolkit, enabling low-resource organizations to launch tiplines for
their communities. Finally, we envision SynDy enabling additional fact-checking
tools such as matching new misinformation claims to high-quality explainers on
common misinformation topics.

SynDy 是一种利用最先进的大型语言模型，通过合成动态数据集的方法，为舆情封控提供了重要工具，用于针对声称匹配、专题聚类和声称关系分类等任务生成细粒度的合成标签，以便进行大规模人工事实核查。