demonstration

LikeMiner: a system for mining the power of 'like' in social media networks

Authors:

Jiawei HanAuthors Info & Claims

KDD '11: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 753 - 756

https://doi.org/10.1145/2020408.2020528

Published: 21 August 2011 Publication History

Abstract

Social media is becoming increasingly ubiquitous and popular on the Internet. Due to the huge popularity of social media websites, such as Facebook, Twitter, YouTube and Flickr, many companies or public figures are now active in maintaining pages on those websites to interact with online users, attracting a large number of fans/followers by posting interesting objects, e.g., (product) photos/videos and text messages. 'Like' has now become a very popular social function by allowing users to express their like of certain objects. It provides an accurate way of estimating user interests and an effective way of sharing/promoting information in social media. In this demo, we propose a system called LikeMiner to mine the power of 'like' in social media networks. We introduce a heterogeneous network model for social media with 'likes', and propose 'like' mining algorithms to estimate representativeness and influence of objects. The implemented prototype system demonstrates the effectiveness of the proposed approach using the large scale Facebook data.

References

[1]

The Open Directory Project (ODP), http://www.dmoz.org.

[2]

S. Aksoy and R. M. Haralick. Textural features for image database retrieval. In CBAIVL '98: Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries, page 45, 1998.

Digital Library

[3]

D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 3:993--1022, 2003.

Digital Library

[4]

W. Chen, C. Wang, and Y. Wang. Scalable influence maximization for prevalent viral marketing in large-scale social networks. In KDD'10, Washington D.C., July 2010.

Digital Library

[5]

R. Datta, D. Joshi, J. Li, and J. Z. Wang. Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys, 40(2):1--60, April 2008.

Digital Library

[6]

T. Deselaers, D. Keysers, and H. Ney. Features for image retrieval: an experimental comparison. Information Retrieval, 11(2):77--107, 2008.

Digital Library

[7]

J. Huang, S. R. Kumar, M. Mitra, W.-J. Zhu, and R. Zabih. Image indexing using color correlograms. In Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97), page 762, 1997.

Digital Library

[8]

X. Jin, S. Kim, J. Han, L. Kao, and Z. Yin. A general framework for efficient clustering of large datasets based on activity detection. Statistical Analysis and Data Mining, 4(1):11--29, 2011.

Digital Library

[9]

X. Jin, J. Luo, J. Yu, G. Wang, D. Joshi, and J. Han. iRIN: image retrieval in image-rich information networks. In M. Rappa, P. Jones, J. Freire, and S. Chakrabarti, editors, WWW, pages 1261--1264. ACM, 2010.

Digital Library

[10]

D. Kempe, J. Kleinberg, and E. Tardos. Maximizing the spread of influence through a social network. In KDD'03, pages 137--146, 2003.

Digital Library

[11]

D. G. Lowe. Object recognition from local scale-invariant features. In Proceedings of the International Conference on Computer Vision-Volume 2, page 1150, 1999.

Digital Library

[12]

R. C. Ltd. MINDS's descriptors for still images - spatial edge distribution descriptor. ISO/IEC/JTC1/SC29/WG11, Lancaster, UK, Feb, page 109, 1999.

[13]

K. Muller and J. R. Ohm. Wavelet-based contour descriptor. ISO/IEC/JTC1/SC29/WG11, Lancaster, UK, Feb., page 567, 1999.

[14]

A. V. Nevel. Texture synthesis via matching first and second order statistics of a wavelet frame decomposition. Image Processing, International Conference on, 1:72, 1998.

[15]

M. Park, J. S. Jin, and L. S. Wilson. Fast content-based image retrieval using quasi-gabor filter and reduction of image feature dimension. In SSIAI '02: Proceedings of the Fifth IEEE Southwest Symposium on Image Analysis and Interpretation, page 178, 2002.

Digital Library

[16]

A. R. Rao and G. L. Lohse. Towards a texture naming system: identifying relevant dimensions of texture. In VIS '93: Proceedings of the 4th conference on Visualization '93, pages 220--227, Washington, DC, USA, 1993. IEEE Computer Society.

Digital Library

[17]

Y. M. Ro, S. Y. Kim, K. W. You, M. Kim, and J. Kim. Texture description using atoms of matching pursuits. ISO/IEC JTC1/SC29/WG11, Lancaster, UK, Feb, page 612, 1999.

[18]

D. M. Squire, W. Muler, H. Muler, and T. Pun. Content-based query of image databases: inspirations from text retrieval. Pattern Recognition Letters, 21(13-14):1193--1198, 2000. Selected Papers from The 11th Scandinavian Conference on Image.

Digital Library

[19]

J. Tang, J. Sun, C. Wang, and Z. Yang. Social influence analysis in large-scale networks. In KDD '09, 2009.

Digital Library

[20]

S.-H. Yang, B. Long, A. Smola, N. Sadagopan, Z. Zheng, and H. Zha. Like like alike: joint friendship and interest propagation in social networks. In Proceedings of the 20th international conference on World wide web, WWW '11, pages 537--546, 2011.

Digital Library

Cited By

Chen HYang CZhang XLiu ZSun MJin J(2021)From Symbols to Embeddings: A Tale of Two Representations in Computational Social ScienceJournal of Social Computing10.23919/JSC.2021.00112:2(103-156)Online publication date: Jun-2021
https://doi.org/10.23919/JSC.2021.0011
Son YChoi Y(2020)Improving Matrix Factorization Based Expert Recommendation for Manuscript Editing Services by Refining User Opinions with Binary RatingsApplied Sciences10.3390/app1010339510:10(3395)Online publication date: 14-May-2020
https://doi.org/10.3390/app10103395
Kim J(2020)The Meaning of Numbers: Effect of Social Media Engagement Metrics in Risk CommunicationCommunication Studies10.1080/10510974.2020.1819842(1-19)Online publication date: 29-Sep-2020
https://doi.org/10.1080/10510974.2020.1819842
Show More Cited By

Index Terms

LikeMiner: a system for mining the power of 'like' in social media networks
1. Information systems
  1. Information storage systems
    1. Storage architectures
      1. Storage network architectures
  2. Information systems applications
    1. Data mining

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cover image ACM Conferences

KDD '11: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2011

1446 pages

ISBN:9781450308137

DOI:10.1145/2020408

General Chair:
Chid Apte
IBM Research
,
Program Chairs:
Joydeep Ghosh
UT Austin
,
Padhraic Smyth
UC Irvine

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 21 August 2011

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KDD '11

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KDD '11: The 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 21 - 24, 2011

California, San Diego, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Chen HYang CZhang XLiu ZSun MJin J(2021)From Symbols to Embeddings: A Tale of Two Representations in Computational Social ScienceJournal of Social Computing10.23919/JSC.2021.00112:2(103-156)Online publication date: Jun-2021
https://doi.org/10.23919/JSC.2021.0011
Son YChoi Y(2020)Improving Matrix Factorization Based Expert Recommendation for Manuscript Editing Services by Refining User Opinions with Binary RatingsApplied Sciences10.3390/app1010339510:10(3395)Online publication date: 14-May-2020
https://doi.org/10.3390/app10103395
Kim J(2020)The Meaning of Numbers: Effect of Social Media Engagement Metrics in Risk CommunicationCommunication Studies10.1080/10510974.2020.1819842(1-19)Online publication date: 29-Sep-2020
https://doi.org/10.1080/10510974.2020.1819842
Jungselius B(2019)“She liked the picture so i think she liked it”. Unpacking the social practice of liking« Elle a liké la photo, alors je pense qu’elle l’a aimée ». Démêler la pratique sociale du likingNetcom10.4000/netcom.3849(23-38)Online publication date: 3-Sep-2019
https://doi.org/10.4000/netcom.3849
Preti GLissandrini MMottin DVelegrakis Y(2019)Mining patterns in graphs with multiple weightsDistributed and Parallel Databases10.1007/s10619-019-07259-wOnline publication date: 18-Feb-2019
https://doi.org/10.1007/s10619-019-07259-w
Patel HParaskevopoulos PRenz M(2018)Data Fusion of Diverse Data SourcesProceedings of the Fifth International ACM SIGMOD Workshop on Managing and Mining Enriched Geo-Spatial Data10.1145/3210272.3210275(13-18)Online publication date: 10-Jun-2018
https://dl.acm.org/doi/10.1145/3210272.3210275
Oh CYergeau S(2017)Social capital, social media, and TV ratingsInternational Journal of Business Information Systems10.1504/IJBIS.2017.08145024:2(242-260)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1504/IJBIS.2017.081450
Zhou HPan Q(2017)Blogging With a Mission, Blogging Within a System: Chinese Government-organized NGOs, Corporate-organized NGOs, Grassroots, and Student Organizations on WeiboSociological Research Online10.1177/136078041772407622:3(95-119)Online publication date: 19-Sep-2017
https://doi.org/10.1177/1360780417724076
Shi CLi YZhang JSun YYu P(2017)A Survey of Heterogeneous Information Network AnalysisIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2016.259856129:1(17-37)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1109/TKDE.2016.2598561
Oh CRoumani YNwankpa JHu H(2017)Beyond likes and tweetsInformation and Management10.1016/j.im.2016.03.00454:1(25-37)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1016/j.im.2016.03.004
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