  The option of sharing images, videos and audio files on social media opens up
new possibilities for distinguishing between false information and fake news on
the Internet. Due to the vast amount of data shared every second on social
media, not all data can be verified by a computer or a human expert. Here, a
check-worthiness analysis can be used as a first step in the fact-checking
pipeline and as a filtering mechanism to improve efficiency. This paper
proposes a novel way of detecting the check-worthiness in multi-modal tweets.
It takes advantage of two classifiers, each trained on a single modality. For
image data, extracting the embedded text with an OCR analysis has shown to
perform best. By combining the two classifiers, the proposed solution was able
to place first in the CheckThat! 2023 Task 1A with an F1 score of 0.7297
achieved on the private test set.
