FEND: Data Mining for Fake News in Social Media: Propagation, Detection and Mitigation
When : 2019-05-02
Where : Alberta, Canada
Submission Deadline : 2019-02-01
Categories : Cloud Computing , Wireless Sensor Networks , Internet Computing , Embedded Systems
http://pike.psu.edu/fend19/
FEND: Data Mining for Fake News in Social Media: Propagation, Detection and Mitigation (SDM 2019)
May 2-4, 2019, Alberta, Canada
Scope & Topics
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.
Topics of Interest :
- 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
Important Dates
| Submission Deadline | : | February 01, 2019 |
| Authors Notification | : | March 15, 2019 |
| Final Manuscript Due | : | April 15, 2019 |
User Name : austin
Posted 21-11-2018 on 23:20:30 AEDT