skip to main content
10.1145/2339530.2339779acmconferencesArticle/Chapter ViewAbstractPublication PageskddConference Proceedingsconference-collections
demonstration

VOXSUP: a social engagement framework

Published: 12 August 2012 Publication History

Abstract

Social media websites are currently central hubs on the Internet. Major online social media platforms are not only places for individual users to socialize but are increasingly more important as channels for companies to advertise, public figures to engage, etc. In order to optimize such advertising and engaging efforts, there is an emerging challenge for knowledge discovery on today's Internet. The goal of knowledge discovery is to understand the entire online social landscape instead of merely summarizing the statistics. To answer this challenge, we have created VOXSUP as a unified social engagement framework. Unlike most existing tools, VOXSUP not only aggregates and filters social data from the Internet, but also provides what we call Voxsupian Knowledge Discovery (VKD). VKD consists of an almost human-level understanding of social conversations at any level of granularity from a single comment sentiment to multi-lingual inter-platform user demographics. Here we describe the technologies that are crucial to VKD, and subsequently go beyond experimental verification and present case studies from our live VOXSUP system.

References

[1]
S. Kim, T. Qin, H. Yu, and T.-Y. Liu. Advertiser-centric approach to understand user click behavior in sponsored search. In CIKM '11. ACM, 2011
[2]
K. Zhang, Y. Cheng, Y. Xie, A. Agrawal, D. Palsetia, K. Lee, and A. Choudhary. SES: Sentiment Elicitation System for Social Media Data, ICDM-SENTIRE '11. IEEE, 2011
[3]
D. M. Blei and J. D. Lafferty. Dynamic topic models. In ICML '06. ACM, 2006
[4]
G. Cormode and M. Hadjieleftheriou. Finding frequent items in data streams. In VLDB. ACM, 2008
[5]
A. Lancichinetti and S Fortunato1. Community detection algorithms: A comparative analysis. Phys. Rev. E 80, 056117. 2009
[6]
J. E. Hirsch. An index to quantify an individual's scientific research output. In PNAS 102 (46). 2005

Cited By

View all
  • (2019)Health Services Data: Big Data Analytics for Deriving Predictive Healthcare InsightsHealth Services Evaluation10.1007/978-1-4939-8715-3_2(3-18)Online publication date: 12-Feb-2019
  • (2017)Health Services Data: Big Data Analytics for Deriving Predictive Healthcare InsightsData and Measures in Health Services Research10.1007/978-1-4899-7673-4_2-1(1-17)Online publication date: 28-Jan-2017
  • (2016)Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials scienceAPL Materials10.1063/1.49468944:5Online publication date: 15-Apr-2016

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
August 2012
1616 pages
ISBN:9781450314626
DOI:10.1145/2339530
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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 August 2012

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. opinion mining
  2. social ranking
  3. topic model

Qualifiers

  • Demonstration

Conference

KDD '12
Sponsor:

Acceptance Rates

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

Upcoming Conference

KDD '25

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 13 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2019)Health Services Data: Big Data Analytics for Deriving Predictive Healthcare InsightsHealth Services Evaluation10.1007/978-1-4939-8715-3_2(3-18)Online publication date: 12-Feb-2019
  • (2017)Health Services Data: Big Data Analytics for Deriving Predictive Healthcare InsightsData and Measures in Health Services Research10.1007/978-1-4899-7673-4_2-1(1-17)Online publication date: 28-Jan-2017
  • (2016)Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials scienceAPL Materials10.1063/1.49468944:5Online publication date: 15-Apr-2016

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media