SpacePhaser: Phase space embedding visual analytics

Ngan Nguyen; Tommy Dang

doi:10.1109/COMPSAC.2019.10213

SpacePhaser: Phase space embedding visual analytics

Ngan Nguyen, Tommy Dang

Computer Science

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

Abstract

In the paper, we focus on the statistical detection and understanding of signaling behavior via various media outlets that are associated with a violent hate crime. Following a body of theory that identifies the links between hate rhetoric with violence, we explore textual patterns of hate speech in traditional and non-traditional media outlets, including television news shows, social media, political blogs, and political forums, to detect an association with hate crime events per time and place. Applying the dynamical system theory, we can characterize the dynamic behaviors of essential keywords. Grouping keyword with similar temporal signatures enables us to highlight important information/topics corresponding to emerging events. We also introduce SpacePhaser, an interactive visual tool for textual pattern analysis. To show the usefulness of SpacePhaser, we demonstrate the results of its applications on various real-world datasets.

Original language	English
Title of host publication	Proceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019
Editors	Vladimir Getov, Jean-Luc Gaudiot, Nariyoshi Yamai, Stelvio Cimato, Morris Chang, Yuuichi Teranishi, Ji-Jiang Yang, Hong Va Leong, Hossian Shahriar, Michiharu Takemoto, Dave Towey, Hiroki Takakura, Atilla Elci, Susumu Takeuchi, Satish Puri
Publisher	IEEE Computer Society
Pages	239-244
Number of pages	6
ISBN (Electronic)	9781728126074
DOIs	https://doi.org/10.1109/COMPSAC.2019.10213
State	Published - Jul 2019
Event	43rd IEEE Annual Computer Software and Applications Conference, COMPSAC 2019 - Milwaukee, United States Duration: Jul 15 2019 → Jul 19 2019

Publication series

Name	Proceedings - International Computer Software and Applications Conference
Volume	2
ISSN (Print)	0730-3157

Conference

Conference	43rd IEEE Annual Computer Software and Applications Conference, COMPSAC 2019
Country/Territory	United States
City	Milwaukee
Period	07/15/19 → 07/19/19

Keywords

Dynamic Graphs
Dynamical System Theory
Entity Recognition
Index Terms—Phase Space
Social Media
Time Series Visualization
Topic Modeling
Visual Analytics

Access to Document

10.1109/COMPSAC.2019.10213

Cite this

Nguyen, N., & Dang, T. (2019). SpacePhaser: Phase space embedding visual analytics. In V. Getov, J-L. Gaudiot, N. Yamai, S. Cimato, M. Chang, Y. Teranishi, J-J. Yang, H. V. Leong, H. Shahriar, M. Takemoto, D. Towey, H. Takakura, A. Elci, S. Takeuchi, & S. Puri (Eds.), Proceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019 (pp. 239-244). Article 8753913 (Proceedings - International Computer Software and Applications Conference; Vol. 2). IEEE Computer Society. https://doi.org/10.1109/COMPSAC.2019.10213

Nguyen, Ngan ; Dang, Tommy. / SpacePhaser : Phase space embedding visual analytics. Proceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019. editor / Vladimir Getov ; Jean-Luc Gaudiot ; Nariyoshi Yamai ; Stelvio Cimato ; Morris Chang ; Yuuichi Teranishi ; Ji-Jiang Yang ; Hong Va Leong ; Hossian Shahriar ; Michiharu Takemoto ; Dave Towey ; Hiroki Takakura ; Atilla Elci ; Susumu Takeuchi ; Satish Puri. IEEE Computer Society, 2019. pp. 239-244 (Proceedings - International Computer Software and Applications Conference).

@inproceedings{3a464b51e662447386bbc50193a030c6,

title = "SpacePhaser: Phase space embedding visual analytics",

abstract = "In the paper, we focus on the statistical detection and understanding of signaling behavior via various media outlets that are associated with a violent hate crime. Following a body of theory that identifies the links between hate rhetoric with violence, we explore textual patterns of hate speech in traditional and non-traditional media outlets, including television news shows, social media, political blogs, and political forums, to detect an association with hate crime events per time and place. Applying the dynamical system theory, we can characterize the dynamic behaviors of essential keywords. Grouping keyword with similar temporal signatures enables us to highlight important information/topics corresponding to emerging events. We also introduce SpacePhaser, an interactive visual tool for textual pattern analysis. To show the usefulness of SpacePhaser, we demonstrate the results of its applications on various real-world datasets.",

keywords = "Dynamic Graphs, Dynamical System Theory, Entity Recognition, Index Terms—Phase Space, Social Media, Time Series Visualization, Topic Modeling, Visual Analytics",

author = "Ngan Nguyen and Tommy Dang",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 43rd IEEE Annual Computer Software and Applications Conference, COMPSAC 2019 ; Conference date: 15-07-2019 Through 19-07-2019",

year = "2019",

month = jul,

doi = "10.1109/COMPSAC.2019.10213",

language = "English",

series = "Proceedings - International Computer Software and Applications Conference",

publisher = "IEEE Computer Society",

pages = "239--244",

editor = "Vladimir Getov and Jean-Luc Gaudiot and Nariyoshi Yamai and Stelvio Cimato and Morris Chang and Yuuichi Teranishi and Ji-Jiang Yang and Leong, {Hong Va} and Hossian Shahriar and Michiharu Takemoto and Dave Towey and Hiroki Takakura and Atilla Elci and Susumu Takeuchi and Satish Puri",

booktitle = "Proceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019",

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Nguyen, N & Dang, T 2019, SpacePhaser: Phase space embedding visual analytics. in V Getov, J-L Gaudiot, N Yamai, S Cimato, M Chang, Y Teranishi, J-J Yang, HV Leong, H Shahriar, M Takemoto, D Towey, H Takakura, A Elci, S Takeuchi & S Puri (eds), Proceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019., 8753913, Proceedings - International Computer Software and Applications Conference, vol. 2, IEEE Computer Society, pp. 239-244, 43rd IEEE Annual Computer Software and Applications Conference, COMPSAC 2019, Milwaukee, United States, 07/15/19. https://doi.org/10.1109/COMPSAC.2019.10213

SpacePhaser: Phase space embedding visual analytics. / Nguyen, Ngan; Dang, Tommy.
Proceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019. ed. / Vladimir Getov; Jean-Luc Gaudiot; Nariyoshi Yamai; Stelvio Cimato; Morris Chang; Yuuichi Teranishi; Ji-Jiang Yang; Hong Va Leong; Hossian Shahriar; Michiharu Takemoto; Dave Towey; Hiroki Takakura; Atilla Elci; Susumu Takeuchi; Satish Puri. IEEE Computer Society, 2019. p. 239-244 8753913 (Proceedings - International Computer Software and Applications Conference; Vol. 2).

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

TY - GEN

T1 - SpacePhaser

T2 - 43rd IEEE Annual Computer Software and Applications Conference, COMPSAC 2019

AU - Nguyen, Ngan

AU - Dang, Tommy

PY - 2019/7

Y1 - 2019/7

N2 - In the paper, we focus on the statistical detection and understanding of signaling behavior via various media outlets that are associated with a violent hate crime. Following a body of theory that identifies the links between hate rhetoric with violence, we explore textual patterns of hate speech in traditional and non-traditional media outlets, including television news shows, social media, political blogs, and political forums, to detect an association with hate crime events per time and place. Applying the dynamical system theory, we can characterize the dynamic behaviors of essential keywords. Grouping keyword with similar temporal signatures enables us to highlight important information/topics corresponding to emerging events. We also introduce SpacePhaser, an interactive visual tool for textual pattern analysis. To show the usefulness of SpacePhaser, we demonstrate the results of its applications on various real-world datasets.

AB - In the paper, we focus on the statistical detection and understanding of signaling behavior via various media outlets that are associated with a violent hate crime. Following a body of theory that identifies the links between hate rhetoric with violence, we explore textual patterns of hate speech in traditional and non-traditional media outlets, including television news shows, social media, political blogs, and political forums, to detect an association with hate crime events per time and place. Applying the dynamical system theory, we can characterize the dynamic behaviors of essential keywords. Grouping keyword with similar temporal signatures enables us to highlight important information/topics corresponding to emerging events. We also introduce SpacePhaser, an interactive visual tool for textual pattern analysis. To show the usefulness of SpacePhaser, we demonstrate the results of its applications on various real-world datasets.

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KW - Index Terms—Phase Space

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M3 - Conference contribution

AN - SCOPUS:85072662173

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SP - 239

EP - 244

BT - Proceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019

A2 - Getov, Vladimir

A2 - Gaudiot, Jean-Luc

A2 - Yamai, Nariyoshi

A2 - Cimato, Stelvio

A2 - Chang, Morris

A2 - Teranishi, Yuuichi

A2 - Yang, Ji-Jiang

A2 - Leong, Hong Va

A2 - Shahriar, Hossian

A2 - Takemoto, Michiharu

A2 - Towey, Dave

A2 - Takakura, Hiroki

A2 - Elci, Atilla

A2 - Takeuchi, Susumu

A2 - Puri, Satish

PB - IEEE Computer Society

Y2 - 15 July 2019 through 19 July 2019

ER -

Nguyen N, Dang T. SpacePhaser: Phase space embedding visual analytics. In Getov V, Gaudiot J-L, Yamai N, Cimato S, Chang M, Teranishi Y, Yang J-J, Leong HV, Shahriar H, Takemoto M, Towey D, Takakura H, Elci A, Takeuchi S, Puri S, editors, Proceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019. IEEE Computer Society. 2019. p. 239-244. 8753913. (Proceedings - International Computer Software and Applications Conference). doi: 10.1109/COMPSAC.2019.10213

SpacePhaser: Phase space embedding visual analytics

Abstract

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Conference

Keywords

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