short-paper

Reaction times for user behavior models in microblogging online social networks

Authors:

Samuel Martins Barbosa Neto,

Maira Athanazio De Cerqueira Gatti,

Paulo Rodrigo Cavalin,

Claudio Santos Pinhanez,

Cicero Nogueira Dos Santos,

Ana Paula AppelAuthors Info & Claims

DUBMOD '13: Proceedings of the 2013 workshop on Data-driven user behavioral modelling and mining from social media

Pages 17 - 20

https://doi.org/10.1145/2513577.2513578

Published: 28 October 2013 Publication History

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Abstract

Online Social Networks (OSNs) have, in recent years, emerged as a new way to communicate, diffuse information, coordinate people, establish relationships, among other possibilities. In this context, being able to understand and predict how users behave and developing appropriate models is a key problem to work with OSNs, concerning from marketing campaigns to social movements, for example. Twitter, for instance, was a heavily explored tool in Obama's 2012 election. In this paper, we explore Obama's Twitter network and model its users behavior, applying a stochastic multi-agent based simulation to reproduce the observed data. We study the effects of different time discretizations when applying a first order Markov Model to learn the user behavior and determine that, for Obama's egocentric network, users present a short reaction time to received messages.

References

[1]

M. A. C. Gatti, A. P. Appel, C. Nogueira dos Santos, C. S. Pinhanez, P. R. Cavalin, and S. Barbosa Neto. Large-scale multi-agent-based modeling and simulation of microblogging-based online social network. In Proceedings of the 14th International Workshop on Multi-Agent-Based Simulation, AAMAS'13, 2013.

Google Scholar

[2]

M. A. C. Gatti, A. P. Appel, C. Nogueira dos Santos, C. S. Pinhanez, P. R. Cavalin, and S. Barbosa Neto. A simulation-based approach to analyze the information diffusion in microblogging online social network. In Proceedings of the 2013 Winter Simulation Conference, WSC'13, 2013. To appear.

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Cited By

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Vongsingthong SBoonkrong SUnger HBen-Othman JGang FLiu JArai M(2017)Modeling dynamic network structure in social networksProceedings of the 3rd International Conference on Communication and Information Processing10.1145/3162957.3163019(395-399)Online publication date: 24-Nov-2017
https://dl.acm.org/doi/10.1145/3162957.3163019
de C. Gatti MVieira Mde Melo JCavalin PPinhanez CBuckley SMiller J(2014)Handling big data on agent-based modeling of online social networks with mapreduceProceedings of the 2014 Winter Simulation Conference10.5555/2693848.2693962(851-862)Online publication date: 7-Dec-2014
https://dl.acm.org/doi/10.5555/2693848.2693962
de C. Gatti MVieira Mde Melo JCavalin PPinhanez C(2014)Handling big data on agent-based modeling of Online Social Networks with MapReduceProceedings of the Winter Simulation Conference 201410.1109/WSC.2014.7019946(851-862)Online publication date: Dec-2014
https://doi.org/10.1109/WSC.2014.7019946

Index Terms

Reaction times for user behavior models in microblogging online social networks
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies

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Published In

DUBMOD '13: Proceedings of the 2013 workshop on Data-driven user behavioral modelling and mining from social media

October 2013

40 pages

ISBN:9781450324175

DOI:10.1145/2513577

Co-chair:
Jalal Mahmud
IBM Research -- Almaden
,
Program Chairs:
James Caverlee
Texas A&M University
,
Jeffrey Nichols
IBM Research -- Almaden
,
John O'Donovan
University of California, Santa Barbara
,
Michelle Zhou
IBM Research -- Almaden

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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Association for Computing Machinery

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Published: 28 October 2013

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CIKM'13: 22nd ACM International Conference on Information and Knowledge Management

October 28, 2013

California, San Francisco, USA

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DUBMOD '13 Paper Acceptance Rate 8 of 12 submissions, 67%;

Overall Acceptance Rate 15 of 20 submissions, 75%

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Vongsingthong SBoonkrong SUnger HBen-Othman JGang FLiu JArai M(2017)Modeling dynamic network structure in social networksProceedings of the 3rd International Conference on Communication and Information Processing10.1145/3162957.3163019(395-399)Online publication date: 24-Nov-2017
https://dl.acm.org/doi/10.1145/3162957.3163019
de C. Gatti MVieira Mde Melo JCavalin PPinhanez CBuckley SMiller J(2014)Handling big data on agent-based modeling of online social networks with mapreduceProceedings of the 2014 Winter Simulation Conference10.5555/2693848.2693962(851-862)Online publication date: 7-Dec-2014
https://dl.acm.org/doi/10.5555/2693848.2693962
de C. Gatti MVieira Mde Melo JCavalin PPinhanez C(2014)Handling big data on agent-based modeling of Online Social Networks with MapReduceProceedings of the Winter Simulation Conference 201410.1109/WSC.2014.7019946(851-862)Online publication date: Dec-2014
https://doi.org/10.1109/WSC.2014.7019946

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A comparative study of users' microblogging behavior on sina weibo and twitter

Microblogging: what and how can we learn from it?

Online authenticity, popularity, and the "Real Me" in a microblogging environment

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