In the past decade, social media has become 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 book, 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. This book is supported by additional materials,
including lecture slides, the complete set of figures, key
references, datasets, tools used in this book, and the source code
of representative algorithms. The readers are encouraged to visit
the book website for the latest information:
http://dmml.asu.edu/dfn/
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