  Automated fact-checking is a crucial task in the governance of internet
content. Although various studies utilize advanced models to tackle this issue,
a significant gap persists in addressing complex real-world rumors and
deceptive claims. To address this challenge, this paper explores the novel task
of flaw-oriented fact-checking, including aspect generation and flaw
identification. We also introduce RefuteClaim, a new framework designed
specifically for this task. Given the absence of an existing dataset, we
present FlawCheck, a dataset created by extracting and transforming insights
from expert reviews into relevant aspects and identified flaws. The
experimental results underscore the efficacy of RefuteClaim, particularly in
classifying and elucidating false claims.
