  As growing usage of social media websites in the recent decades, the amount
of news articles spreading online rapidly, resulting in an unprecedented scale
of potentially fraudulent information. Although a plenty of studies have
applied the supervised machine learning approaches to detect such content, the
lack of gold standard training data has hindered the development. Analysing the
single data format, either fake text description or fake image, is the
mainstream direction for the current research. However, the misinformation in
real-world scenario is commonly formed as a text-image pair where the news
article/news title is described as text content, and usually followed by the
related image. Given the strong ability of learning features without labelled
data, contrastive learning, as a self-learning approach, has emerged and
achieved success on the computer vision. In this paper, our goal is to explore
the constrastive learning in the domain of misinformation identification. We
developed a self-learning model and carried out the comprehensive experiments
on a public data set named COSMOS. Comparing to the baseline classifier, our
model shows the superior performance of non-matched image-text pair detection
(approximately 10%) when the training data is insufficient. In addition, we
observed the stability for contrsative learning and suggested the use of it
offers large reductions in the number of training data, whilst maintaining
comparable classification results.
