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Conference Paper: Incorporating the position of sharing action in predicting popular videos in online social networks

TitleIncorporating the position of sharing action in predicting popular videos in online social networks
Authors
KeywordsOnline Social Networks
Information Diffusion
Video
Prediction
Issue Date2014
PublisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/
Citation
The 15th International Conference on Web Information System Engineering, Thessaloniki, Greece, 12-14 October 2014. In Lecture Notes in Computer Science, 2014, v. 8787, p. 125-140 How to Cite?
AbstractPredicting popular videos in online social networks (OSNs) is important for network traffic engineering and video recommendation. In order to avoid the difficulty of acquiring all OSN users’ activities, recent studies try to predict popular media contents in OSNs only based on a very small number of users, referred to as experts. However, these studies simply treat all users’ diffusion actions as the same. Based on large-scale video diffusion traces collected from a popular OSN, we analyze the positions of users’ video sharing actions in the propagation graph, and classify users’ video sharing actions into three different types, i.e., initiator actions, spreader actions and follower actions. Surprisingly, while existing studies mainly focus on the initiators, our empirical studies suggest that the spreaders actually play a more important role in the diffusion process of popular videos. Motivated by this finding, we account for the position information of sharing actions to select initiator experts, spreader experts and follower experts, based on corresponding sharing actions. We conduct experiments on the collected dataset to evaluate the performance of these three types of experts in predicting popular videos. The evaluation results demonstrate that the spreader experts can not only make more accurate predictions than initiator experts and follower experts, but also outperform the general experts selected by existing studies.
DescriptionLNCS v.8787 entitled: Web Information Systems Engineering - WISE 2014: 15th International Conference, Thessaloniki, Greece, October 12-14, 2014, Proceedings, Part 2
Persistent Identifierhttp://hdl.handle.net/10722/217392
ISBN
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252

 

DC FieldValueLanguage
dc.contributor.authorLong, Y-
dc.contributor.authorLi, VOK-
dc.contributor.authorNiu, G-
dc.date.accessioned2015-09-18T05:58:18Z-
dc.date.available2015-09-18T05:58:18Z-
dc.date.issued2014-
dc.identifier.citationThe 15th International Conference on Web Information System Engineering, Thessaloniki, Greece, 12-14 October 2014. In Lecture Notes in Computer Science, 2014, v. 8787, p. 125-140-
dc.identifier.isbn978-3-319-11745-4-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/217392-
dc.descriptionLNCS v.8787 entitled: Web Information Systems Engineering - WISE 2014: 15th International Conference, Thessaloniki, Greece, October 12-14, 2014, Proceedings, Part 2-
dc.description.abstractPredicting popular videos in online social networks (OSNs) is important for network traffic engineering and video recommendation. In order to avoid the difficulty of acquiring all OSN users’ activities, recent studies try to predict popular media contents in OSNs only based on a very small number of users, referred to as experts. However, these studies simply treat all users’ diffusion actions as the same. Based on large-scale video diffusion traces collected from a popular OSN, we analyze the positions of users’ video sharing actions in the propagation graph, and classify users’ video sharing actions into three different types, i.e., initiator actions, spreader actions and follower actions. Surprisingly, while existing studies mainly focus on the initiators, our empirical studies suggest that the spreaders actually play a more important role in the diffusion process of popular videos. Motivated by this finding, we account for the position information of sharing actions to select initiator experts, spreader experts and follower experts, based on corresponding sharing actions. We conduct experiments on the collected dataset to evaluate the performance of these three types of experts in predicting popular videos. The evaluation results demonstrate that the spreader experts can not only make more accurate predictions than initiator experts and follower experts, but also outperform the general experts selected by existing studies.-
dc.languageeng-
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/-
dc.relation.ispartofLecture Notes in Computer Science-
dc.rightsThe final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-11746-1_9-
dc.subjectOnline Social Networks-
dc.subjectInformation Diffusion-
dc.subjectVideo-
dc.subjectPrediction-
dc.titleIncorporating the position of sharing action in predicting popular videos in online social networks-
dc.typeConference_Paper-
dc.identifier.emailLong, Y: yilong@eee.hku.hk-
dc.identifier.emailLi, VOK: vli@eee.hku.hk-
dc.identifier.emailNiu, G: gilniu@eee.hku.hk-
dc.identifier.authorityLi, VOK=rp00150-
dc.description.naturepostprint-
dc.identifier.doi10.1007/978-3-319-11746-1_9-
dc.identifier.scopuseid_2-s2.0-84921664323-
dc.identifier.hkuros254349-
dc.identifier.volume8787-
dc.identifier.spage125-
dc.identifier.epage140-
dc.publisher.placeGermany-
dc.customcontrol.immutablesml 151126-

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