Abstract:
"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."
