The expansion of online social media platforms has led to a surge in online
content consumption. However, this has also paved the way for disseminating
false claims and misinformation. As a result, there is an escalating demand for
a substantial workforce to sift through and validate such unverified claims.
Currently, these claims are manually verified by fact-checkers. Still, the
volume of online content often outweighs their potency, making it difficult for
them to validate every single claim in a timely manner. Thus, it is critical to
determine which assertions are worth fact-checking and prioritize claims that
require immediate attention. Multiple factors contribute to determining whether
a claim necessitates fact-checking, encompassing factors such as its factual
correctness, potential impact on the public, the probability of inciting
hatred, and more. Despite several efforts to address claim check-worthiness, a
systematic approach to identify these factors remains an open challenge. To
this end, we introduce a new task of fine-grained claim check-worthiness, which
underpins all of these factors and provides probable human grounds for
identifying a claim as check-worthy. We present CheckIt, a manually annotated
large Twitter dataset for fine-grained claim check-worthiness. We benchmark our
dataset against a unified approach, CheckMate, that jointly determines whether
a claim is check-worthy and the factors that led to that conclusion. We compare
our suggested system with several baseline systems. Finally, we report a
thorough analysis of results and human assessment, validating the efficacy of
integrating check-worthiness factors in detecting claims worth fact-checking.Comment: 28 pages, 2 figures, 8 table