Workshop on Data Mining for Fake News in Social Media: Propagation, Detection, and Mitigation (FEND'19)@ SDM 2019
FEND 2019
- URL: http://pike.psu.edu/fend19/
- Event Date: 2019-05-02 ~ 2019-05-04
- Submission Date: 2019-02-01
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: February 1, 2019
Notification: March 15, 2019
SDM pre-registration deadline: April 2, 2019
Camera ready: April 15, 2019
Conference dates: May 2-4, 2019
Shall you have any questions, please email to szw494@psu.edu or kai.shu@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
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: February 1, 2019
Notification: March 15, 2019
SDM pre-registration deadline: April 2, 2019
Camera ready: April 15, 2019
Conference dates: May 2-4, 2019
Shall you have any questions, please email to szw494@psu.edu or kai.shu@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
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