TY - JOUR
AU - Liu, Huan
AB -  Fake News Detection on Social Media: A Data Mining Perspective Kai Shu , Amy Sliva ¡ , Suhang Wang , Jiliang Tang , and Huan Liu Computer Science & Engineering, Arizona State University, Tempe, AZ, USA ¡ Charles River Analytics, Cambridge, MA, USA Computer Science & Engineering, Michigan State University, East Lansing, MI, USA {kai.shu,suhang.wang,huan.liu}@asu.edu, ¡ asliva@cra.com, tangjili@msu.edu ABSTRACT Social media for news consumption is a double-edged sword. On the one hand, its low cost, easy access, and rapid dissemination of information lead people to seek out and consume news from social media. On the other hand, it enables the wide spread of œfake news , i.e., low quality news with intentionally false information. The extensive spread of fake news has the potential for extremely negative impacts on individuals and society. Therefore, fake news detection on social media has recently become an emerging research that is attracting tremendous attention. Fake news detection on social media presents unique characteristics and challenges that make existing detection algorithms from traditional news media ine €ective or not applicable. First, fake news is intentionally written to mislead readers to believe false information, which makes it di ƒcult and nontrivial to detect based on 
TI - Fake News Detection on Social Media: A Data Mining Perspective
JF - ACM SIGKDD Explorations Newsletter
DO - 10.1145/3137597.3137600
DA - 2017-09-01
UR - https://www.deepdyve.com/lp/association-for-computing-machinery/fake-news-detection-on-social-media-a-data-mining-perspective-3pcyMHvnGk
SP - 22
VL - 19
IS - 1
DP - DeepDyve
ER -