Popularity prediction of social multimedia based on concept drift

Shih Hong Jheng, Cheng-Te Li, Hsi Lin Chen, Man Kwan Shan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings - SocialCom/PASSAT/BigData/EconCom/BioMedCom 2013
Pages821-826
Number of pages6
DOIs
Publication statusPublished - 2013 Dec 1
Event2013 ASE/IEEE Int. Conf. on Social Computing, SocialCom 2013, the 2013 ASE/IEEE Int. Conf. on Big Data, BigData 2013, the 2013 Int. Conf. on Economic Computing, EconCom 2013, the 2013 PASSAT 2013, and the 2013 ASE/IEEE Int. Conf. on BioMedCom 2013 - Washington, DC, United States
Duration: 2013 Sep 82013 Sep 14

Publication series

NameProceedings - SocialCom/PASSAT/BigData/EconCom/BioMedCom 2013

Other

Other2013 ASE/IEEE Int. Conf. on Social Computing, SocialCom 2013, the 2013 ASE/IEEE Int. Conf. on Big Data, BigData 2013, the 2013 Int. Conf. on Economic Computing, EconCom 2013, the 2013 PASSAT 2013, and the 2013 ASE/IEEE Int. Conf. on BioMedCom 2013
CountryUnited States
CityWashington, DC
Period13-09-0813-09-14

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All Science Journal Classification (ASJC) codes

  • Software

Cite this

Jheng, S. H., Li, C-T., Chen, H. L., & Shan, M. K. (2013). Popularity prediction of social multimedia based on concept drift. In Proceedings - SocialCom/PASSAT/BigData/EconCom/BioMedCom 2013 (pp. 821-826). [6693420] (Proceedings - SocialCom/PASSAT/BigData/EconCom/BioMedCom 2013). https://doi.org/10.1109/SocialCom.2013.123