Google Research

FauxBuster: A Content-free Fauxtography Detector Using Social Media Comments

  • Daniel Zhang
  • Lanyu Shang
  • Biao Geng
  • Shuyue Lai
  • Ke Li
  • Hongmin Zhu
  • Tanvir Amin
  • Dong Wang
Proceedings of IEEE BigData 2018 (to appear)


With the increasing popularity of online social media (e.g., Facebook, Twitter, Reddit), the detection of misleading content on social media has become a critical problem. This paper focuses on an important but largely unsolved problem: detecting fauxtography (i.e., social media posts with misleading images). We found that the existing literature falls short in solving this problem. In particular, current solutions either focus on the detection of fake images or misinformed texts of a social media post. However, they cannot solve our problem because the detection of fauxtography depends not only on the truthfulness of the images and the texts but also on the information they deliver together on the posts. In this paper, we develop the FauxBuster, an end-to-end supervised learning scheme that can effectively track down fauxtography by exploring the valuable clues from user’s comments of a post on social media. The FauxBuster is content-free in that it does not rely on the analysis of the actual content of the images, and hence is robust against sophisticated uploaders who can intentionally modify the description and presentation of the images. We evaluate FauxBuster on real-world datasets collected from two mainstream social media platforms - Reddit and Twitter. The results show our scheme is both effective and efficient in addressing the fauxtography problem.

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