[LNSN] Fake News, Disinformation, and Misinformation in Social Media

-Emerging Research Challenges and Opportunities

Book Editors

Book Description

Social media has become a popular means to information seeking and news consuming. Because it is cheap to provide news online and much faster and easier to disseminate through social media, large volumes of disinformation such as fake news, i.e., those news articles with intentionally false information, are produced online for a variety of purposes, such as financial and political gain. The extensive spread of fake news can have severe negative impacts on individuals and society. First, fake news can break the authenticity balance of the news ecosystem. For example, it is evident that the most popular fake news was even more widely spread on Facebook than the most popular authentic mainstream news during the U.S. 2016 presidential election. Second, fake news intentionally persuades consumers to accept biased or false beliefs for political or financial gain. For example, in 2013, $130 billion in stock value was wiped out in a matter of minutes following an Associated Press (AP) tweet about an explosion that injured Barack Obama. AP said its Twitter account was hacked. Third, fake news changes the way people interpret and respond to real news, impeding their abilities to differentiate what is true from what is not. Therefore, it’s critical to understand how fake news propagate, developing data mining techniques for efficient and accurate fake news detection and intervene the propagation of fake news to mitigate the negative effects.


This book aims to bring together researchers, practitioners and social media providers for understanding fake news propagation, improving fake news detection in social media and mitigation. Topic areas include (but are not limited to) the following:


Authors are invited to submit original, unpublished manuscripts that are not previously published, accepted to be published, or being considered for publication in any other forum. All papers should follow the manuscript preparation guidelines for the Springer Lecture Notes in Social Network Analysis submissions, see Instructions for Authors section at https://www.springer.com/series/8768. The submitted chapters should not exceed 20 pages in length. The acceptance will be decided by a peer-review process.

The authors are requested to submit their manuscripts via the online submission manuscript system, available at: https://cmt3.research.microsoft.com/FNDM2019

Important Dates


All questions about submissions should be emailed to Kai Shu at kai.shu at asu.edu