Fact-Checking Meets Fauxtography: Verifying Claims About Images
This addresses the challenge of verifying image-related claims in social media and the Web, which is incremental as it builds on existing fact-checking research by focusing on a neglected aspect.
The paper tackles the problem of automatically fact-checking claims about images, which are often ignored despite their influence in fake news, by creating a new dataset and exploring features modeling the claim, image, and their relationship, resulting in sizable improvements over the baseline.
The recent explosion of false claims in social media and on the Web in general has given rise to a lot of manual fact-checking initiatives. Unfortunately, the number of claims that need to be fact-checked is several orders of magnitude larger than what humans can handle manually. Thus, there has been a lot of research aiming at automating the process. Interestingly, previous work has largely ignored the growing number of claims about images. This is despite the fact that visual imagery is more influential than text and naturally appears alongside fake news. Here we aim at bridging this gap. In particular, we create a new dataset for this problem, and we explore a variety of features modeling the claim, the image, and the relationship between the claim and the image. The evaluation results show sizable improvements over the baseline. We release our dataset, hoping to enable further research on fact-checking claims about images.