  The wide use of social media and digital technologies facilitates sharing
various news and information about events and activities. Despite sharing
positive information misleading and false information is also spreading on
social media. There have been efforts in identifying such misleading
information both manually by human experts and automatic tools. Manual effort
does not scale well due to the high volume of information, containing factual
claims, are appearing online. Therefore, automatically identifying check-worthy
claims can be very useful for human experts. In this study, we describe our
participation in Subtask-1A: Check-worthiness of tweets (English, Dutch and
Spanish) of CheckThat! lab at CLEF 2022. We performed standard preprocessing
steps and applied different models to identify whether a given text is worthy
of fact checking or not. We use the oversampling technique to balance the
dataset and applied SVM and Random Forest (RF) with TF-IDF representations. We
also used BERT multilingual (BERT-m) and XLM-RoBERTa-base pre-trained models
for the experiments. We used BERT-m for the official submissions and our
systems ranked as 3rd, 5th, and 12th in Spanish, Dutch, and English,
respectively. In further experiments, our evaluation shows that transformer
models (BERT-m and XLM-RoBERTa-base) outperform the SVM and RF in Dutch and
English languages where a different scenario is observed for Spanish.
