从网站截图中识别错误信息
Identifying Misinformation from Website Screenshots
摘要：
一个网站的外观和感觉能提供关于一篇文章可信度的信息吗？在本文中，我们建议使用一个有前途的，但被忽视的方面在检测错误信息：域网页的整体外观。为了捕捉这一整体外观，我们截取了由错误信息或值得信赖的Web域提供的新闻文章的屏幕截图，并利用基于张量分解的半监督分类技术。所提出的方法，即，VizFake对许多图像转换不敏感，例如将图像转换为灰度，矢量化图像和丢失屏幕截图的某些部分。VizFake利用了非常少量的已知标签，反映了现实和实际的场景，其中标签（特别是已知的错误信息文章）是稀缺的，并且很快就会过时。VizFake在500多个领域的新闻文章的5万个屏幕截图数据集上的F1得分大约为85%，仅使用5%的地面真实标签。此外，以无监督方式获得的VizFake的张量表示允许对数据进行探索性分析，为问题提供有价值的见解。最后，我们将VizFake与深度迁移学习进行了比较，因为它是一种非常流行的图像分类黑箱方法，也是众所周知的基于文本的方法。VizFake通过深度迁移学习模型实现了具有竞争力的准确性，同时速度快了两个数量级，并且不需要费力的超参数调整。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
