  In recent years, we witness the explosion of false and unconfirmed
information (i.e., rumors) that went viral on social media and shocked the
public. Rumors can trigger versatile, mostly controversial stance expressions
among social media users. Rumor verification and stance detection are different
yet relevant tasks. Fake news debunking primarily focuses on determining the
truthfulness of news articles, which oversimplifies the issue as fake news
often combines elements of both truth and falsehood. Thus, it becomes crucial
to identify specific instances of misinformation within the articles. In this
research, we investigate a novel task in the field of fake news debunking,
which involves detecting sentence-level misinformation. One of the major
challenges in this task is the absence of a training dataset with
sentence-level annotations regarding veracity. Inspired by the Multiple
Instance Learning (MIL) approach, we propose a model called Weakly Supervised
Detection of Misinforming Sentences (WSDMS). This model only requires bag-level
labels for training but is capable of inferring both sentence-level
misinformation and article-level veracity, aided by relevant social media
conversations that are attentively contextualized with news sentences. We
evaluate WSDMS on three real-world benchmarks and demonstrate that it
outperforms existing state-of-the-art baselines in debunking fake news at both
the sentence and article levels.
