"There is a wealth of knowledge available online. Some are trustworthy, while others are

deceptive and phony. The need to identify such false information arises from the danger it

poses to society at a mass. Nowadays, there is a significant need for information that requires

fact-checking. As a result, we need a layer preceding fact-checking, where it can be determined

whether a claim is check-worthy. This will streamline the automated fact-checking process by

filtering out a lot of unnecessary data that is nonetheless necessary. We carried out such a study

as part of CLEF 2023 CheckThat! Lab (CTL) task 1B, where we were provided with a dataset

of tweets and debate snippets and were asked to conduct an experiment to verify whether a

particular news tweet/debate snippet is check worthy. The dataset contains 3 languages

(English, Arabic, Spanish). We used several machine learning and deep learning algorithms in

our experiments. Among them, XLM-RoBERTa which outperformed other algorithms for

English and Arabic but for Spanish we found that Logistic Regression can outperform other

models."