  Can the look and the feel of a website give information about the
trustworthiness of an article? In this paper, we propose to use a promising,
yet neglected aspect in detecting the misinformativeness: the overall look of
the domain webpage. To capture this overall look, we take screenshots of news
articles served by either misinformative or trustworthy web domains and
leverage a tensor decomposition based semi-supervised classification technique.
The proposed approach i.e., VizFake is insensitive to a number of image
transformations such as converting the image to grayscale, vectorizing the
image and losing some parts of the screenshots. VizFake leverages a very small
amount of known labels, mirroring realistic and practical scenarios, where
labels (especially for known misinformative articles), are scarce and quickly
become dated. The F1 score of VizFake on a dataset of 50k screenshots of news
articles spanning more than 500 domains is roughly 85% using only 5% of ground
truth labels. Furthermore, tensor representations of VizFake, obtained in an
unsupervised manner, allow for exploratory analysis of the data that provides
valuable insights into the problem. Finally, we compare VizFake with deep
transfer learning, since it is a very popular black-box approach for image
classification and also well-known text text-based methods. VizFake achieves
competitive accuracy with deep transfer learning models while being two orders
of magnitude faster and not requiring laborious hyper-parameter tuning.
