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In the past decade, social media is becoming increasingly popular for news consumption due to its easy access, fast dissemination, and low cost. However, social media also enables the wide propagation of ``fake news'', i.e., news with intentionally false information. Fake news on social media can have significant negative societal effects. Therefore, fake news detection on social media has recently become an emerging research area that is attracting tremendous attention. This lecture, from a data mining perspective, introduces the basic concepts and characteristics of fake news across disciplines, reviews representative fake news detection methods in a principled way, and illustrates challenging issues of fake news detection on social media. In particular, we discussed the value of news content and social context, and important extensions to handle early detection, weakly-supervised detection, and explainable detection. The concepts, algorithms, and methods described in this lecture can help harness the power of social media to build effective and intelligent fake news detection systems. This book is an accessible introduction to the study of detecting fake news on social media. It is an essential reading for students, researchers, and practitioners to understand, manage, and excel in this area.

To cite this book, please use this bibtex entry:

@article{shu2019dfn,
  title={Detecting Fake News on Social Media},
  author={Shu, Kai and Liu, Huan},
  journal={Synthesis Lectures on Data Mining and Knowledge Discovery},
  year={2019},
  publisher={Morgan \& Claypool Publishers}
}