More Web Proxy on the site http://driver.im/

research-article

Modeling mass protest adoption in social network communities using geometric brownian motion

Authors:

Rupinder Paul Khandpur,

Edward Dougherty,

B. Aditya Prakash,

Naren RamakrishnanAuthors Info & Claims

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 1660 - 1669

https://doi.org/10.1145/2623330.2623376

Published: 24 August 2014 Publication History

Abstract

Modeling the movement of information within social media outlets, like Twitter, is key to understanding to how ideas spread but quantifying such movement runs into several difficulties. Two specific areas that elude a clear characterization are (i) the intrinsic random nature of individuals to potentially adopt and subsequently broadcast a Twitter topic, and (ii) the dissemination of information via non-Twitter sources, such as news outlets and word of mouth, and its impact on Twitter propagation. These distinct yet inter-connected areas must be incorporated to generate a comprehensive model of information diffusion. We propose a bispace model to capture propagation in the union of (exclusively) Twitter and non-Twitter environments. To quantify the stochastic nature of Twitter topic propagation, we combine principles of geometric Brownian motion and traditional network graph theory. We apply Poisson process functions to model information diffusion outside of the Twitter mentions network. We discuss techniques to unify the two sub-models to accurately model information dissemination. We demonstrate the novel application of these techniques on real Twitter datasets related to mass protest adoption in social communities.

Supplementary Material

MP4 File (p1660-sidebyside.mp4)

Download
218.38 MB

References

[1]

V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10):P10008, 2008.

[2]

R. M. Bond, C. J. Fariss, J. J. Jones, A. D. Kramer, C. Marlow, J. E. Settle, and J. H. Fowler. A 61-million-person experiment in social influence and political mobilization. Nature, 489(7415):295--298, 2012.

[3]

H. Castellini and L. Romanelli. On the propagation of social epidemics in social networks under SIR model. eprint arXiv:nlin/0703053, Mar. 2007.

[4]

M. D. Conover, E. Ferrara, F. Menczer, and A. Flammini. The digital evolution of occupy wall street. PloS one, 8(5):e64679, 2013.

[5]

R. Crane and D. Sornette. Robust dynamic classes revealed by measuring the response function of a social system. PNAS, 105(41):15649--15653, 2008.

[6]

S. González-Bailön, J. Borge-Holthoefer, A. Rivero, and Y. Moreno. The dynamics of protest recruitment through an online network. Scientific reports, 1, 2011.

[7]

A. Guille, H. Hacid, C. Favre, and D. A. Zighed. Information diffusion in online social networks: A survey. ACM SIGMOD Record, 42(1):17--28, 2013.

Digital Library

[8]

H. Zhou. Network landscape from a brownian particle's perspective. Physical Review E, 67(4):041908, 2003.

[9]

F. A. Haight and F. A. Haight. Handbook of the Poisson distribution. Wiley New York, 1967.

[10]

A. L. Hill, D. G. Rand, M. A. Nowak, and N. A. Christakis. Emotions as infectious diseases in a large social network: the sisa model. Proc. Royal Society B: Biological Sciences, 277(1701):3827--3835, 2010.

[11]

T. Iwata, A. Shah, and Z. Ghahramani. Discovering latent influence in online social activities via shared cascade poisson processes. In Proc. KDD'13, pages 266--274, 2013.

Digital Library

[12]

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

Digital Library

[13]

Y. Matsubara, Y. Sakurai, B. A. Prakash, L. Li, and C. Faloutsos. Rise and fall patterns of information diffusion: model and implications. In Proc. KDD'12, pages 6--14. ACM, 2012.

Digital Library

[14]

S. A. Myers, C. Zhu, and J. Leskovec. Information diffusion and external influence in networks. In Proc. KDD'12, pages 33--41, 2012.

Digital Library

[15]

M. E. Newman. The structure and function of complex networks. SIAM review, 45(2):167--256, 2003.

Digital Library

[16]

M. E. Newman. Modularity and community structure in networks. PNAS, 103(23):8577--8582, 2006.

[17]

B. Øksendal. Stochastic differential equations. Springer, 2003.

[18]

P. E. Oliver and D. J. Myers. Diffusion models of cycles of protest as a theory of social movements. Presented at the Congress of the International Sociological Association, 1998.

[19]

M. G. Rodriguez, J. Leskovec, and B. Scholkopf. Modeling information propagation with survival theory. In Proc. ICML'13, pages 666--674, 2013.

[20]

A. Saad-Filho. Mass protests under 'left neoliberalism': Brazil, june-july 2013. Critical Sociology, 39(5):657--669, 2013.

[21]

J. Scott and P. J. Carrington. The SAGE handbook of social network analysis. SAGE publications, 2011.

Digital Library

[22]

Y. Singer. How to win friends and influence people, truthfully: influence maximization mechanisms for social networks. In Proc. WSDM'12, pages 733--742. ACM, 2012.

Digital Library

[23]

P. Tankov. Financial modelling with jump processes. CRC Press, 2004.

[24]

Z. Tufekci and C. Wilson. Social media and the decision to participate in political protest: Observations from Tahrir square. Journal of Communication, 62(2):363--379, 2012.

[25]

U. F. Wiersema. Brownian motion calculus. Wiley.com, 2008.

[26]

H. Zhou. Distance, dissimilarity index, and network community structure. Physical Review E, 67(6):061901, 2003.

[27]

H. Zhou and R. Lipowsky. Network brownian motion: A new method to measure vertex-vertex proximity and to identify communities and subcommunities. In Proc. ICCS'04, pages 1062--1069. Springer, 2004.

Cited By

Zanin MTrajanovski PJolakoski PSandev TKocarev L(2024)Evaluating Time Irreversibility Tests Using Geometric Brownian Motions with Stochastic ResettingSymmetry10.3390/sym1611144516:11(1445)Online publication date: 31-Oct-2024
https://doi.org/10.3390/sym16111445
Deng Sde Rijke MNing YBaeza-Yates RBonchi F(2024)Advances in Human Event Modeling: From Graph Neural Networks to Language ModelsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671466(6459-6469)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671466
Liang CCheng WDotter TParkhurst Z(2023)Sabotage-proof Messenger Discovery for Protesters based on Brownian motion and Erasure Coding2023 19th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)10.1109/WiMob58348.2023.10187796(460-465)Online publication date: 21-Jun-2023
https://doi.org/10.1109/WiMob58348.2023.10187796
Show More Cited By

Index Terms

Modeling mass protest adoption in social network communities using geometric brownian motion
1. Information systems
  1. Information systems applications
    1. Data mining

Recommendations

What is Twitter, a social network or a news media?
WWW '10: Proceedings of the 19th international conference on World wide web

Twitter, a microblogging service less than three years old, commands more than 41 million users as of July 2009 and is growing fast. Twitter users tweet about any topic within the 140-character limit and follow others to receive their tweets. The goal ...
Analyzing and modeling dynamics of information diffusion in microblogging social network

Among different types of the pervasive social networks, microblogging social network recently provides most efficient services for diffusing information of news, ideas and innovations. The features and models of information diffusion in microblogging ...
Finding influentials based on the temporal order of information adoption in twitter
WWW '10: Proceedings of the 19th international conference on World wide web

Twitter offers an explicit mechanism to facilitate information diffusion and has emerged as a new medium for communication. Many approaches to find influentials have been proposed, but they do not consider the temporal order of information adoption. In ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2014

2028 pages

ISBN:9781450329569

DOI:10.1145/2623330

General Chairs:
Sofus Macskassy
Facebook
,
Claudia Perlich
Dstillery
,
Program Chairs:
Jure Leskovec
Stanford University
,
Wei Wang
UCLA
,
Rayid Ghani
University of Chicago

Copyright © 2014 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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 August 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Division of Information and Intelligent Systems
The Intelligence Advanced Research Projects Activity (IARPA)

Conference

KDD '14

Sponsor:

KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 24 - 27, 2014

New York, New York, USA

Acceptance Rates

KDD '14 Paper Acceptance Rate 151 of 1,036 submissions, 15%;

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

Upcoming Conference

KDD '25

Sponsor:
sigkdd
sigkdd

The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

Toronto , ON , Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

18
Total Citations
View Citations
560
Total Downloads

Downloads (Last 12 months)15
Downloads (Last 6 weeks)0

Reflects downloads up to 28 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zanin MTrajanovski PJolakoski PSandev TKocarev L(2024)Evaluating Time Irreversibility Tests Using Geometric Brownian Motions with Stochastic ResettingSymmetry10.3390/sym1611144516:11(1445)Online publication date: 31-Oct-2024
https://doi.org/10.3390/sym16111445
Deng Sde Rijke MNing YBaeza-Yates RBonchi F(2024)Advances in Human Event Modeling: From Graph Neural Networks to Language ModelsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671466(6459-6469)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671466
Liang CCheng WDotter TParkhurst Z(2023)Sabotage-proof Messenger Discovery for Protesters based on Brownian motion and Erasure Coding2023 19th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)10.1109/WiMob58348.2023.10187796(460-465)Online publication date: 21-Jun-2023
https://doi.org/10.1109/WiMob58348.2023.10187796
Goode BPires B(2022)Encoding Protest Duration In An Agent-Based Model As Characteristic Phase Transitions2022 Annual Modeling and Simulation Conference (ANNSIM)10.23919/ANNSIM55834.2022.9859477(164-175)Online publication date: 18-Jul-2022
https://doi.org/10.23919/ANNSIM55834.2022.9859477
Oncevay ASobrevilla MAlatrista-Salas HMelgar A(2020)Public Riots in Twitter: Domain-Based Event Filtering During Civil UnrestADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium10.1007/978-3-030-55814-7_4(49-59)Online publication date: 18-Aug-2020
https://doi.org/10.1007/978-3-030-55814-7_4
Dang TNguyen VTominski Cvon Landesberger T(2018)ComModelerProceedings of the EuroVis Workshop on Visual Analytics10.5555/3290753.3290755(1-5)Online publication date: 4-Jun-2018
https://dl.acm.org/doi/10.5555/3290753.3290755
Ranganath SBeigi GLiu HChang YZhai CLiu YMaarek Y(2018)Leveraging Implicit Contribution Amounts to Facilitate Microfinancing RequestsProceedings of the Eleventh ACM International Conference on Web Search and Data Mining10.1145/3159652.3159679(477-485)Online publication date: 2-Feb-2018
https://dl.acm.org/doi/10.1145/3159652.3159679
Korkmaz GKuhlman CRavi SVega-Redondo F(2018)Spreading of social contagions without key playersWorld Wide Web10.1007/s11280-017-0500-y21:5(1187-1221)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s11280-017-0500-y
Yang ZNguyen LStuve JCao GJin F(2017)Harvey flooding rescue in social media2017 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2017.8258166(2177-2185)Online publication date: Dec-2017
https://doi.org/10.1109/BigData.2017.8258166
Nguyen LSalopek AZhao LJin F(2017)A natural language normalization approach to enhance social media text reasoning2017 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2017.8258148(2019-2026)Online publication date: Dec-2017
https://doi.org/10.1109/BigData.2017.8258148
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten