[AISWorld] Last CFP: SDM 2019 FEND Workshop, May 2-4, 2019, Alberta, Canada

[Kai Shu]

[Please accept our apologies if you receive multiple copies of this CFP]

Call for Papers

Workshop on Data Mining for Fake News in Social Media: Propagation,
Detection, and Mitigation (FEND'19), in conjunction with SDM'19
http://pike.psu.edu/fend19/
May 2-4, 2019, Alberta, Canada

Social media has become a popular means to consume news. However, the
quality of news on social media is lower than traditional news
organizations. Because it is cheap to provide news online and much faster
and easier to disseminate through social media, large volumes of 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.

The objectives of this workshop are:

 - Bring together researchers from both academia and industry as well as
practitioners to present their latest problems and ideas;
 - Attract social media providers who have access to interesting sources of
fake news datasets and problems but lack the expertise in data mining to
use data effectively;
 - Enhance interactions between data mining, text mining, social media
mining, and sociology and psychology communities working on problems of
fake news propagation, detection, and mitigation.

This workshop 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 for the workshop include (but are not limited to) the following:

 - User behavior analysis and characterization for fake news detection
 - Text mining - mining news contents and user comments
 - Early fake news detection
 - Unsupervised fake news detection
 - 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

Paper Submission:

Papers should be submitted as PDF, using the SIAM conference proceedings
style, available at
https://www.siam.org/Portals/0/Publications/Proceedings/soda2e_061418.zip?ver=2018-06-15-102100-887.
Submissions should be limited to nine pages and submitted via CMT at
https://cmt3.research.microsoft.com/FEND2019.


Important Dates:
Submission deadline: March 30, 2019
Notification: April 10, 2019
SDM pre-registration deadline: April 20, 2019
Conference dates: May 2-4, 2019

Shall you have any questions, please email to szw494 at psu.edu or
kai.shu at asu.edu.

Workshop Organizers:
Suhang Wang   Penn State University, USA
Dongwon Lee   Penn State University, USA
Huan Liu   Arizona State University, USA

Workshop Publicity Chair:
Kai Shu   Arizona State University, USA