  The widespread diffusion of medical and political claims in the wake of
COVID-19 has led to a voluminous rise in misinformation and fake news. The
current vogue is to employ manual fact-checkers to efficiently classify and
verify such data to combat this avalanche of claim-ridden misinformation.
However, the rate of information dissemination is such that it vastly outpaces
the fact-checkers' strength. Therefore, to aid manual fact-checkers in
eliminating the superfluous content, it becomes imperative to automatically
identify and extract the snippets of claim-worthy (mis)information present in a
post. In this work, we introduce the novel task of Claim Span Identification
(CSI). We propose CURT, a large-scale Twitter corpus with token-level claim
spans on more than 7.5k tweets. Furthermore, along with the standard token
classification baselines, we benchmark our dataset with DABERTa, an
adapter-based variation of RoBERTa. The experimental results attest that
DABERTa outperforms the baseline systems across several evaluation metrics,
improving by about 1.5 points. We also report detailed error analysis to
validate the model's performance along with the ablation studies. Lastly, we
release our comprehensive span annotation guidelines for public use.
