  Public, professional and academic interest in automated fact-checking has
drastically increased over the past decade, with many aiming to automate one of
the first steps in a fact-check procedure: the selection of so-called
checkworthy claims. However, there is little agreement on the definition and
characteristics of checkworthiness among fact-checkers, which is consequently
reflected in the datasets used for training and testing checkworthy claim
detection models. After elaborate analysis of checkworthy claim selection
procedures in fact-check organisations and analysis of state-of-the-art claim
detection datasets, checkworthiness is defined as the concept of having a
spatiotemporal and context-dependent worth and need to have the correctness of
the objectivity it conveys verified. This is irrespective of the claim's
perceived veracity judgement by an individual based on prior knowledge and
beliefs. Concerning the characteristics of current datasets, it is argued that
the data is not only highly imbalanced and noisy, but also too limited in scope
and language. Furthermore, we believe that the subjective concept of
checkworthiness might not be a suitable filter for claim detection.
