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.

翻译：网站的外观和感觉能否提供有关文章可信度的信息? 在本文中, 我们提议使用一个有希望但被忽视的方面来发现错误信息化: 域网页的总体外观 。 为了捕捉这一总体外观, 我们拍摄了由错误信息化或可信赖的网络域所服务的新闻文章的截图, 并运用基于半监督的分类技术。 提议的方法, 即 VizFake, 对一些图像转换的图像转换不敏感, 例如将图像转换为灰度、 向图像矢量 和 屏幕截图的某些部分丢失 。 VizFake 利用了很少数量的已知标签, 反映了现实和实用的情景, 其中标签( 特别是已知的不可靠信息化文章) 十分稀少且迅速过时。 在50k 截图中, 覆盖超过500 个域的新闻文章的 F1 评分, 大约85% 使用快速的地面真相标签 。 此外, VizFake 的演示, 要求以不精确的模型获取。 以不精确的黑度方式获取的 VizF 。 。, 也允许通过探索性的分析 分析 数据, 提供宝贵的文本分析, 最后, 和深层次的解的图像 。