DocumentCode :
1821509
Title :
Some computational challenges in mining social media
Author :
Huan Liu
Author_Institution :
Data Min. & Machine Learning Lab., Arizona State Univ., Tempe, AZ, USA
fYear :
2013
fDate :
25-28 Aug. 2013
Abstract :
People of all walks of life use social media for communications and networking. Their active participation in numerous and diverse online activities continually generates massive amounts of social media data. This undoubtedly “big” data presents new challenges to data mining, including how to select salient features for social media data with varied relations, how to assess user vulnerability, and how to ensure that patterns discovered from social media data are valid when no ground truth is available. We will illustrate the intricacies of social media data, present original social-computing problems, deliberate approaches to mining social media data to gain insight from real-world applications and deepen our understanding, and exploit unique characteristics of social media data in developing novel algorithms and computational tools for social media mining.
Keywords :
data mining; social networking (online); data mining; salient feature; social media; social-computing problem; user vulnerability;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advances in Social Networks Analysis and Mining (ASONAM), 2013 IEEE/ACM International Conference on
Conference_Location :
Niagara Falls, ON
Type :
conf
Filename :
6785672
Link To Document :
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