[AISWorld] Call for Book Chapters - [LNSN] Fake News, Disinformation, and Misinformation in Social Media

[Kai Shu]

Call for book chapters
Book on Fake News, Disinformation, and Misinformation in Social Media
http://www.public.asu.edu/~skai2/fndm.html.
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 in the propagation of fake news to mitigate the
negative effects.
Topics

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:

   - User behavior analysis and characterization for misinformation and
   fake news detection
   - Text mining – mining news contents and user comments
   - Early fake news detection
   - Semi-supervised and Unsupervised fake news detection
   - Efficient and Effective Fact-checking
   - Tracing and characterizing the propagation of fake news and true news
   - Malicious account and bot detection, user credibility assessment
   - Visual analysis and exploration with images on the news
   - News event aggregation and detection
   - Building benchmark datasets for fake news detection in social media

Submission

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

   - Submission deadline: September 1, 2019
   - Notification: November 1, 2019
   - Camera-ready deadline: December 15, 2019

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

*Editors*

Kai Shu Arizona State University, USA
Suhang Wang   Penn State University, USA
Dongwon Lee   Penn State University, USA
Huan Liu   Arizona State University, USA