NDLI: Popularity Prediction of Social Multimedia Based on Concept Drift

Content Provider	IEEE Xplore Digital Library
Author	Shih-Hong Jheng Cheng-Te Li Hsi-Lin Chen Man-Kwan Shan
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. Sci., Nat. Chengchi Univ., Taipei, Taiwan (Shih-Hong Jheng; Hsi-Lin Chen; Man-Kwan Shan) \|\| Grad. Inst. of Networking & Multimedia, Nat. Taiwan Univ., Taipei, Taiwan (Cheng-Te Li)
Abstract	Microblogging services such as Twitter and Plurk allow users to easily access and share different types of social multimedia (e.g. images and videos) over the online social world. However, information overload happens to users and prohibits them from reaching popular and important digital contents. This paper studies the problem of predicting the popularity of social multimedia which is embedded in short messages of microblogging social networks. Social multimedia exhibits the property that they might be persistently or periodically re-shared and thus their popularity might resurrect at some time and evolve over time. We exploit the idea of concept drift to capture this property. We formulate the problem using classification, and propose to tackle the tasks of Re-share classification and Popularity Score classification. Two categories of features are devised and extracted, including information diffusion and explicit multimedia meta information. We develop a concept drift-based popularity predictor, by ensembling multiple trained classifiers from social multimedia instances in different time intervals. The key lies in dynamically determining the ensemble weights of classifiers. Experiments conducted on the Plurk data show the high accuracy on the popularity classification and the promising results on detecting popular social multimedia.
Starting Page	821
Ending Page	826
File Size	550572
Page Count	6
File Format	PDF
ISBN	9780769551371
DOI	10.1109/SocialCom.2013.123
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Popularity prediction Social multimedia Concept drift Streaming media Feature extraction Twitter Social network Microblog Multimedia communication Information diffusion YouTube Videos
Content Type	Text
Resource Type	Article

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