Fake News Detection: Misleading Headlines and Satire

Authors

  • Andrew Wang
  • Phillip Guo
  • Ethan Wong

Keywords:

fake news detection, machine learning, misleading headline, satire, social media

Abstract

During recent years, the spread of fake news and incorrect information across the web has been an increasingly severe problem. Since most social media platforms (Twitter included) allow their users to post text and pictures freely, spreading fake news is extremely easy and those who spread fake news are rarely punished. Due to the absence of systems upholding the integrity of social media posts, social media applications treat these posts the same as they would a post with correct information. In addition, misleading headlines can have a dramatic impact on the spread of fake news; headlines can be exaggerated and misleading to a viewer. Experiments have shown that misleading headlines can impact a reader's memory, which further influence's a reader's opinion. Unlike fake news, however, satirical news is used in an entertaining way and is not meant to deceive its viewers. Satirical news is a form of media used to criticize and mock an individual, idea, or a topic. Although satirical news’ intention is not to manipulate one’s opinion, studies have shown that it indeed can.

Published

2021-10-15

How to Cite

Wang, A., Guo, P., & Wong, E. (2021). Fake News Detection: Misleading Headlines and Satire. International Journal of Computational and Biological Intelligent Systems, 3(2). Retrieved from https://ijcbis.org/index.php/ijcbis/article/view/1641