Identifying Misinformation from Website Screenshots

Authors

  • Sara Abdali Department of Computer Science and Engineering University of California
  • Rutuja Gurav Department of Computer Science and Engineering University of California
  • Siddharth Menon Department of Computer Science and Engineering University of California
  • Daniel Fonseca Department of Computer Science and Engineering University of California
  • Negin Entezari Department of Computer Science and Engineering University of California
  • Neil Shah Snap Inc.
  • Evangelos E. Papalexakis Department of Computer Science and Engineering University of California

Keywords:

Web and Social Media

Abstract

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 web page. 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 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.

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Published

2021-05-22

How to Cite

Abdali, S., Gurav, R., Menon, S., Fonseca, D., Entezari, N., Shah, N., & Papalexakis, E. E. (2021). Identifying Misinformation from Website Screenshots. Proceedings of the International AAAI Conference on Web and Social Media, 15(1), 2-13. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/18036