research-article

Learning from the crowd: an evolutionary mutual reinforcement model for analyzing events

Authors:

Debanjan Mahata,

Nitin AgarwalAuthors Info & Claims

ASONAM '13: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Pages 474 - 478

https://doi.org/10.1145/2492517.2492661

Published: 25 August 2013 Publication History

Abstract

Social media is inarguably a powerful medium for mobilizing support for various real-life events be it for social, political, or economic transformation. Further, in contrast to the generic information obtained from the mainstream media, novel and specific information available at social media sites makes them valuable sources for event analysis. However, due to the power law distribution of the Internet, these overwhelmingly large number of sources are buried in the Long Tail making it extremely challenging to identify the quality sources among them. In this research, we propose an evolutionary mutual reinforcement model to confront these challenges. Due to absence of ground truth, a novel evaluation strategy is introduced. The results indicate tremendous potential. 25% to 130% information gain is obtained with the proposed approach when compared against the state-of-the-art baselines, viz. Google blog search and Icerocket blog search. Further, our ranking methodology is capable of identifying the highly informative sources much earlier than the aforementioned baselines. The proposed model affords an apparatus for micro and macro event analysis.

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Cited By

Mahata DAgarwal N(2015)Identifying Event-Specific Sources from Social MediaOnline Social Media Analysis and Visualization10.1007/978-3-319-13590-8_1(1-25)Online publication date: 15-Jan-2015
https://doi.org/10.1007/978-3-319-13590-8_1
Raad EChbeir R(2014)Foto2EventsProceedings of the 2014 IEEE Fourth International Conference on Big Data and Cloud Computing10.1109/BDCloud.2014.76(508-515)Online publication date: 3-Dec-2014
https://dl.acm.org/doi/10.1109/BDCloud.2014.76

Index Terms

Learning from the crowd: an evolutionary mutual reinforcement model for analyzing events
1. Computing methodologies
  1. Machine learning
  2. Modeling and simulation
    1. Model development and analysis
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Web-based interaction

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cover image ACM Conferences

ASONAM '13: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

August 2013

1558 pages

ISBN:9781450322409

DOI:10.1145/2492517

General Chairs:
Jon Rokne
University of Calgary, Calgary, AB, Canada
,
Christos Faloutsos
Carnegie Mellon University, Pittsburgh, PA

Copyright © 2013 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: 25 August 2013

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ASONAM '13: Advances in Social Networks Analysis and Mining 2013

August 25 - 28, 2013

Ontario, Niagara, Canada

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Overall Acceptance Rate 116 of 549 submissions, 21%

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Cited By

Mahata DAgarwal N(2015)Identifying Event-Specific Sources from Social MediaOnline Social Media Analysis and Visualization10.1007/978-3-319-13590-8_1(1-25)Online publication date: 15-Jan-2015
https://doi.org/10.1007/978-3-319-13590-8_1
Raad EChbeir R(2014)Foto2EventsProceedings of the 2014 IEEE Fourth International Conference on Big Data and Cloud Computing10.1109/BDCloud.2014.76(508-515)Online publication date: 3-Dec-2014
https://dl.acm.org/doi/10.1109/BDCloud.2014.76

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