Deep Agent: Studying the Dynamics of Information Spread and Evolution in Social Networks

Ivan Garibay; Toktam A. Oghaz; Niloofar Yousefi; Ece Çiğdem Mutlu; Madeline Schiappa; Steven Scheinert; Georgios C. Anagnostopoulos; Christina Bouwens; Stephen M. Fiore; Alexander Mantzaris; John T. Murphy; William Rand; Anastasia Salter; Mel Stanfill; Gita Sukthankar; Nisha Baral; Gabriel Fair; Chathika Gunaratne; Neda B. Hajiakhoond; Jasser Jasser; Chathura Jayalath; Olivia B. Newton; Samaneh Saadat; Chathurani Senevirathna; Rachel Winter; Xi Zhang

doi:10.1007/978-3-030-77517-9_11

Deep Agent: Studying the Dynamics of Information Spread and Evolution in Social Networks

Ivan Garibay
, Toktam A. Oghaz
, Niloofar Yousefi
, Ece Çiğdem Mutlu
, Madeline Schiappa
, Steven Scheinert
, Georgios C. Anagnostopoulos
, Christina Bouwens
, Stephen M. Fiore
, Alexander Mantzaris
, John T. Murphy
, William Rand
, Anastasia Salter
, Mel Stanfill
, Gita Sukthankar
, Nisha Baral
, Gabriel Fair
, Chathika Gunaratne
, Neda B. Hajiakhoond
, Jasser Jasser

Chathura Jayalath, Olivia B. Newton, Samaneh Saadat, Chathurani Senevirathna, Rachel Winter, Xi Zhang

Learning Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

This paper explains the design of a social network analysis framework, developed under DARPA’s SocialSim program, with novel architecture that models human emotional, cognitive, and social factors. Our framework is both theory and data-driven, and utilizes domain expertise. Our simulation effort helps understanding how information flows and evolves in social media platforms. We focused on modeling three information domains: cryptocurrencies, cyber threats, and software vulnerabilities for the three interrelated social environments: GitHub, Reddit, and Twitter. We participated in the SocialSim DARPA Challenge in December 2018, in which our models were subjected to an extensive performance evaluation for accuracy, generalizability, explainability, and experimental power. This paper reports the main concepts and models, utilized in our social media modeling effort in developing a multi-resolution simulation at the user, community, population, and content levels.

Original language	English
Title of host publication	Proceedings of the 2019 International Conference of The Computational Social Science Society of the Americas
Editors	Zining Yang, Elizabeth von Briesen
Publisher	Springer Science and Business Media B.V.
Pages	153-169
Number of pages	17
ISBN (Print)	9783030775162
DOIs	https://doi.org/10.1007/978-3-030-77517-9_11
State	Published - 2021
Event	International Conference of the Computational Social Science Society of the Americas, CSSSA 2019 - Santa Fe, United States Duration: Oct 24 2019 → Oct 27 2019

Publication series

Name	Springer Proceedings in Complexity
ISSN (Print)	2213-8684
ISSN (Electronic)	2213-8692

Conference

Conference	International Conference of the Computational Social Science Society of the Americas, CSSSA 2019
Country/Territory	United States
City	Santa Fe
Period	10/24/19 → 10/27/19

Funding

Acknowledgements This work was supported by the Defense Advanced Research Projects Agency (DARPA) under grant number FA8650-18-C-7823. The views and opinions expressed in this article are the authors’ own and should not be construed as official or as reflecting the views of the University of Central Florida, DARPA, or the U.S. Department of Defense. This work was supported by the Defense Advanced Research Projects Agency (DARPA) under grant number FA8650-18-C-7823. The views and opinions expressed in this article are the authors? own and should not be construed as official or as reflecting the views of the University of Central Florida, DARPA, or the U.S. Department of Defense.

Access to Document

10.1007/978-3-030-77517-9_11

Cite this

Garibay, I., Oghaz, T. A., Yousefi, N., Mutlu, E. Ç., Schiappa, M., Scheinert, S., Anagnostopoulos, G. C., Bouwens, C., Fiore, S. M., Mantzaris, A., Murphy, J. T., Rand, W., Salter, A., Stanfill, M., Sukthankar, G., Baral, N., Fair, G., Gunaratne, C., Hajiakhoond, N. B., ... Zhang, X. (2021). Deep Agent: Studying the Dynamics of Information Spread and Evolution in Social Networks. In Z. Yang, & E. von Briesen (Eds.), Proceedings of the 2019 International Conference of The Computational Social Science Society of the Americas (pp. 153-169). (Springer Proceedings in Complexity). Springer Science and Business Media B.V.. https://doi.org/10.1007/978-3-030-77517-9_11

Garibay, Ivan ; Oghaz, Toktam A. ; Yousefi, Niloofar et al. / Deep Agent : Studying the Dynamics of Information Spread and Evolution in Social Networks. Proceedings of the 2019 International Conference of The Computational Social Science Society of the Americas. editor / Zining Yang ; Elizabeth von Briesen. Springer Science and Business Media B.V., 2021. pp. 153-169 (Springer Proceedings in Complexity).

@inproceedings{e8dc0f75a125490a8da22f3fa59a3b00,

title = "Deep Agent: Studying the Dynamics of Information Spread and Evolution in Social Networks",

abstract = "This paper explains the design of a social network analysis framework, developed under DARPA{\textquoteright}s SocialSim program, with novel architecture that models human emotional, cognitive, and social factors. Our framework is both theory and data-driven, and utilizes domain expertise. Our simulation effort helps understanding how information flows and evolves in social media platforms. We focused on modeling three information domains: cryptocurrencies, cyber threats, and software vulnerabilities for the three interrelated social environments: GitHub, Reddit, and Twitter. We participated in the SocialSim DARPA Challenge in December 2018, in which our models were subjected to an extensive performance evaluation for accuracy, generalizability, explainability, and experimental power. This paper reports the main concepts and models, utilized in our social media modeling effort in developing a multi-resolution simulation at the user, community, population, and content levels.",

author = "Ivan Garibay and Oghaz, \{Toktam A.\} and Niloofar Yousefi and Mutlu, \{Ece {\c C}iğdem\} and Madeline Schiappa and Steven Scheinert and Anagnostopoulos, \{Georgios C.\} and Christina Bouwens and Fiore, \{Stephen M.\} and Alexander Mantzaris and Murphy, \{John T.\} and William Rand and Anastasia Salter and Mel Stanfill and Gita Sukthankar and Nisha Baral and Gabriel Fair and Chathika Gunaratne and Hajiakhoond, \{Neda B.\} and Jasser Jasser and Chathura Jayalath and Newton, \{Olivia B.\} and Samaneh Saadat and Chathurani Senevirathna and Rachel Winter and Xi Zhang",

note = "Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Conference of the Computational Social Science Society of the Americas, CSSSA 2019 ; Conference date: 24-10-2019 Through 27-10-2019",

year = "2021",

doi = "10.1007/978-3-030-77517-9\_11",

language = "English",

isbn = "9783030775162",

series = "Springer Proceedings in Complexity",

publisher = "Springer Science and Business Media B.V.",

pages = "153--169",

editor = "Zining Yang and \{von Briesen\}, Elizabeth",

booktitle = "Proceedings of the 2019 International Conference of The Computational Social Science Society of the Americas",

}

Garibay, I, Oghaz, TA, Yousefi, N, Mutlu, EÇ, Schiappa, M, Scheinert, S, Anagnostopoulos, GC, Bouwens, C, Fiore, SM, Mantzaris, A, Murphy, JT, Rand, W, Salter, A, Stanfill, M, Sukthankar, G, Baral, N, Fair, G, Gunaratne, C, Hajiakhoond, NB, Jasser, J, Jayalath, C, Newton, OB, Saadat, S, Senevirathna, C, Winter, R & Zhang, X 2021, Deep Agent: Studying the Dynamics of Information Spread and Evolution in Social Networks. in Z Yang & E von Briesen (eds), Proceedings of the 2019 International Conference of The Computational Social Science Society of the Americas. Springer Proceedings in Complexity, Springer Science and Business Media B.V., pp. 153-169, International Conference of the Computational Social Science Society of the Americas, CSSSA 2019, Santa Fe, United States, 10/24/19. https://doi.org/10.1007/978-3-030-77517-9_11

Deep Agent: Studying the Dynamics of Information Spread and Evolution in Social Networks. / Garibay, Ivan; Oghaz, Toktam A.; Yousefi, Niloofar et al.
Proceedings of the 2019 International Conference of The Computational Social Science Society of the Americas. ed. / Zining Yang; Elizabeth von Briesen. Springer Science and Business Media B.V., 2021. p. 153-169 (Springer Proceedings in Complexity).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Deep Agent

T2 - International Conference of the Computational Social Science Society of the Americas, CSSSA 2019

AU - Garibay, Ivan

AU - Oghaz, Toktam A.

AU - Yousefi, Niloofar

AU - Mutlu, Ece Çiğdem

AU - Schiappa, Madeline

AU - Scheinert, Steven

AU - Anagnostopoulos, Georgios C.

AU - Bouwens, Christina

AU - Fiore, Stephen M.

AU - Mantzaris, Alexander

AU - Murphy, John T.

AU - Rand, William

AU - Salter, Anastasia

AU - Stanfill, Mel

AU - Sukthankar, Gita

AU - Baral, Nisha

AU - Fair, Gabriel

AU - Gunaratne, Chathika

AU - Hajiakhoond, Neda B.

AU - Jasser, Jasser

AU - Jayalath, Chathura

AU - Newton, Olivia B.

AU - Saadat, Samaneh

AU - Senevirathna, Chathurani

AU - Winter, Rachel

AU - Zhang, Xi

PY - 2021

Y1 - 2021

N2 - This paper explains the design of a social network analysis framework, developed under DARPA’s SocialSim program, with novel architecture that models human emotional, cognitive, and social factors. Our framework is both theory and data-driven, and utilizes domain expertise. Our simulation effort helps understanding how information flows and evolves in social media platforms. We focused on modeling three information domains: cryptocurrencies, cyber threats, and software vulnerabilities for the three interrelated social environments: GitHub, Reddit, and Twitter. We participated in the SocialSim DARPA Challenge in December 2018, in which our models were subjected to an extensive performance evaluation for accuracy, generalizability, explainability, and experimental power. This paper reports the main concepts and models, utilized in our social media modeling effort in developing a multi-resolution simulation at the user, community, population, and content levels.

AB - This paper explains the design of a social network analysis framework, developed under DARPA’s SocialSim program, with novel architecture that models human emotional, cognitive, and social factors. Our framework is both theory and data-driven, and utilizes domain expertise. Our simulation effort helps understanding how information flows and evolves in social media platforms. We focused on modeling three information domains: cryptocurrencies, cyber threats, and software vulnerabilities for the three interrelated social environments: GitHub, Reddit, and Twitter. We participated in the SocialSim DARPA Challenge in December 2018, in which our models were subjected to an extensive performance evaluation for accuracy, generalizability, explainability, and experimental power. This paper reports the main concepts and models, utilized in our social media modeling effort in developing a multi-resolution simulation at the user, community, population, and content levels.

UR - https://www.scopus.com/pages/publications/85117126375

U2 - 10.1007/978-3-030-77517-9_11

DO - 10.1007/978-3-030-77517-9_11

M3 - Conference contribution

AN - SCOPUS:85117126375

SN - 9783030775162

T3 - Springer Proceedings in Complexity

SP - 153

EP - 169

BT - Proceedings of the 2019 International Conference of The Computational Social Science Society of the Americas

A2 - Yang, Zining

A2 - von Briesen, Elizabeth

PB - Springer Science and Business Media B.V.

Y2 - 24 October 2019 through 27 October 2019

ER -

Garibay I, Oghaz TA, Yousefi N, Mutlu EÇ, Schiappa M, Scheinert S et al. Deep Agent: Studying the Dynamics of Information Spread and Evolution in Social Networks. In Yang Z, von Briesen E, editors, Proceedings of the 2019 International Conference of The Computational Social Science Society of the Americas. Springer Science and Business Media B.V. 2021. p. 153-169. (Springer Proceedings in Complexity). doi: 10.1007/978-3-030-77517-9_11

Deep Agent: Studying the Dynamics of Information Spread and Evolution in Social Networks

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