Sampling social streams for hot social events analytics

Ye Li; Fan Xia; Chengcheng Yu; Weining Qian; Aoying Zhou

doi:10.1109/ICDEW.2015.7129576

Sampling social streams for hot social events analytics

Ye Li
, Fan Xia
, Chengcheng Yu
, Weining Qian
, Aoying Zhou

School of Data Science and Engineering

East China Normal University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Various analytical methods are applied on social media data for opinion mining, user recommondation, product advertising, and etc. They share the common requirement on collecting massive social media data, among which messages on hotspot social events are mostly valuable for understanding users' intensions. Apparently, sampling the global timeline evenly cannot meet the requirement, because it may miss important messages. In this paper, a sampling method for social streams, named as RS³, for Real-time Social-Stream Sampler, is introduced. It adaptively samples the global timeline as well as hotspotting messages. Preliminary empirical study shows the effectiveness of RS³. We also show its application in a prototype system for social event analytics.

Original language	English
Title of host publication	ICDEW 2015 - 2015 IEEE 31st International Conference on Data Engineering Workshops
Publisher	IEEE Computer Society
Pages	198-201
Number of pages	4
ISBN (Electronic)	9781479984411
DOIs	https://doi.org/10.1109/ICDEW.2015.7129576
State	Published - 19 Jun 2015
Event	2015 31st IEEE International Conference on Data Engineering Workshops, ICDEW 2015 - Seoul, Korea, Republic of Duration: 13 Apr 2015 → 17 Apr 2015

Publication series

Name	Proceedings - International Conference on Data Engineering
Volume	2015-June
ISSN (Print)	1084-4627

Conference

Conference	2015 31st IEEE International Conference on Data Engineering Workshops, ICDEW 2015
Country/Territory	Korea, Republic of
City	Seoul
Period	13/04/15 → 17/04/15

Access to Document

10.1109/ICDEW.2015.7129576

Cite this

Li, Y., Xia, F., Yu, C., Qian, W., & Zhou, A. (2015). Sampling social streams for hot social events analytics. In ICDEW 2015 - 2015 IEEE 31st International Conference on Data Engineering Workshops (pp. 198-201). Article 7129576 (Proceedings - International Conference on Data Engineering; Vol. 2015-June). IEEE Computer Society. https://doi.org/10.1109/ICDEW.2015.7129576

@inproceedings{b0bf83277381490297745b3e9672d9f0,

title = "Sampling social streams for hot social events analytics",

abstract = "Various analytical methods are applied on social media data for opinion mining, user recommondation, product advertising, and etc. They share the common requirement on collecting massive social media data, among which messages on hotspot social events are mostly valuable for understanding users' intensions. Apparently, sampling the global timeline evenly cannot meet the requirement, because it may miss important messages. In this paper, a sampling method for social streams, named as RS3, for Real-time Social-Stream Sampler, is introduced. It adaptively samples the global timeline as well as hotspotting messages. Preliminary empirical study shows the effectiveness of RS3. We also show its application in a prototype system for social event analytics.",

author = "Ye Li and Fan Xia and Chengcheng Yu and Weining Qian and Aoying Zhou",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 2015 31st IEEE International Conference on Data Engineering Workshops, ICDEW 2015 ; Conference date: 13-04-2015 Through 17-04-2015",

year = "2015",

month = jun,

day = "19",

doi = "10.1109/ICDEW.2015.7129576",

language = "英语",

series = "Proceedings - International Conference on Data Engineering",

publisher = "IEEE Computer Society",

pages = "198--201",

booktitle = "ICDEW 2015 - 2015 IEEE 31st International Conference on Data Engineering Workshops",

address = "美国",

}

Li, Y, Xia, F, Yu, C, Qian, W & Zhou, A 2015, Sampling social streams for hot social events analytics. in ICDEW 2015 - 2015 IEEE 31st International Conference on Data Engineering Workshops., 7129576, Proceedings - International Conference on Data Engineering, vol. 2015-June, IEEE Computer Society, pp. 198-201, 2015 31st IEEE International Conference on Data Engineering Workshops, ICDEW 2015, Seoul, Korea, Republic of, 13/04/15. https://doi.org/10.1109/ICDEW.2015.7129576

Sampling social streams for hot social events analytics. / Li, Ye; Xia, Fan; Yu, Chengcheng et al.
ICDEW 2015 - 2015 IEEE 31st International Conference on Data Engineering Workshops. IEEE Computer Society, 2015. p. 198-201 7129576 (Proceedings - International Conference on Data Engineering; Vol. 2015-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - Various analytical methods are applied on social media data for opinion mining, user recommondation, product advertising, and etc. They share the common requirement on collecting massive social media data, among which messages on hotspot social events are mostly valuable for understanding users' intensions. Apparently, sampling the global timeline evenly cannot meet the requirement, because it may miss important messages. In this paper, a sampling method for social streams, named as RS3, for Real-time Social-Stream Sampler, is introduced. It adaptively samples the global timeline as well as hotspotting messages. Preliminary empirical study shows the effectiveness of RS3. We also show its application in a prototype system for social event analytics.

AB - Various analytical methods are applied on social media data for opinion mining, user recommondation, product advertising, and etc. They share the common requirement on collecting massive social media data, among which messages on hotspot social events are mostly valuable for understanding users' intensions. Apparently, sampling the global timeline evenly cannot meet the requirement, because it may miss important messages. In this paper, a sampling method for social streams, named as RS3, for Real-time Social-Stream Sampler, is introduced. It adaptively samples the global timeline as well as hotspotting messages. Preliminary empirical study shows the effectiveness of RS3. We also show its application in a prototype system for social event analytics.

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T2 - 2015 31st IEEE International Conference on Data Engineering Workshops, ICDEW 2015

Y2 - 13 April 2015 through 17 April 2015

ER -

Sampling social streams for hot social events analytics

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