Exploring the country co-occurrence network in the twittersphere at an international economic event

Xinzhi Zhang

doi:10.1007/978-981-10-6805-8_25

Exploring the country co-occurrence network in the twittersphere at an international economic event

Xinzhi Zhang^*

^*Corresponding author for this work

Department of Journalism

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

Abstract

This paper explores how international relations are represented on social media in the context of an international economic event, specifically the “Belt and Road Initiative” proposed by the government of mainland China. The present study focuses on the country co-occurrence network represented in the Twittersphere, such that a link is established between two countries if they appear in the same tweet. The study also investigates how the formation of such a network can be explained by geographical, political, and economic factors. An application programming interface (API) harvested all relevant public tweets (n = 26,515) in a one-month time span (2 June–28 June 2017). The names of the countries or regions were extracted to establish the network, with 52 nodes (countries or regions) and 86 edges. Social network analysis revealed that mainland China, Hong Kong, Pakistan, Greece, Kenya, and Iran were in the network’s important positions, as indicated by their high betweenness centrality. Exponential random graph modeling (ERGM) results suggested that West Asian countries engaging heavily in international polities, countries with lower levels of press freedom, and those receiving less direct investment from mainland China, were more likely to be tweeted together.

Original language	English
Title of host publication	Social Media Processing - 6th National Conference, SMP 2017, Proceedings
Editors	Xueqi Cheng, Weiying Ma, Huan Liu, Huawei Shen, Shizheng Feng, Xing Xie
Publisher	Springer Verlag
Pages	308-318
Number of pages	11
Edition	1st
ISBN (Electronic)	9789811068058
ISBN (Print)	9789811068041
DOIs	https://doi.org/10.1007/978-981-10-6805-8_25
Publication status	Published - 24 Oct 2017
Event	6th National Conference on Social Media Processing, SMP 2017 - Beijing, China Duration: 14 Sept 2017 → 17 Sept 2017

Publication series

Name	Communications in Computer and Information Science
Volume	774
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	6th National Conference on Social Media Processing, SMP 2017
Country/Territory	China
City	Beijing
Period	14/09/17 → 17/09/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 16 Peace, Justice and Strong Institutions

User-Defined Keywords

Country co-occurrence network
Exponential random graph models
International relations
Social network analysis
Text mining
Twittersphere

Access to Document

10.1007/978-981-10-6805-8_25

Cite this

Zhang, X. (2017). Exploring the country co-occurrence network in the twittersphere at an international economic event. In X. Cheng, W. Ma, H. Liu, H. Shen, S. Feng, & X. Xie (Eds.), Social Media Processing - 6th National Conference, SMP 2017, Proceedings (1st ed., pp. 308-318). (Communications in Computer and Information Science; Vol. 774). Springer Verlag. https://doi.org/10.1007/978-981-10-6805-8_25

Zhang, Xinzhi. / Exploring the country co-occurrence network in the twittersphere at an international economic event. Social Media Processing - 6th National Conference, SMP 2017, Proceedings. editor / Xueqi Cheng ; Weiying Ma ; Huan Liu ; Huawei Shen ; Shizheng Feng ; Xing Xie. 1st. ed. Springer Verlag, 2017. pp. 308-318 (Communications in Computer and Information Science).

@inproceedings{c0ea7fdc89cb4fe998b11a0f21690136,

title = "Exploring the country co-occurrence network in the twittersphere at an international economic event",

abstract = "This paper explores how international relations are represented on social media in the context of an international economic event, specifically the “Belt and Road Initiative” proposed by the government of mainland China. The present study focuses on the country co-occurrence network represented in the Twittersphere, such that a link is established between two countries if they appear in the same tweet. The study also investigates how the formation of such a network can be explained by geographical, political, and economic factors. An application programming interface (API) harvested all relevant public tweets (n = 26,515) in a one-month time span (2 June–28 June 2017). The names of the countries or regions were extracted to establish the network, with 52 nodes (countries or regions) and 86 edges. Social network analysis revealed that mainland China, Hong Kong, Pakistan, Greece, Kenya, and Iran were in the network{\textquoteright}s important positions, as indicated by their high betweenness centrality. Exponential random graph modeling (ERGM) results suggested that West Asian countries engaging heavily in international polities, countries with lower levels of press freedom, and those receiving less direct investment from mainland China, were more likely to be tweeted together.",

keywords = "Country co-occurrence network, Exponential random graph models, International relations, Social network analysis, Text mining, Twittersphere",

author = "Xinzhi Zhang",

note = "Funding Information: Acknowledgments. The study was partly funded by the Start-up Grant for New Academics (no. RC-1617-1-A2) by Hong Kong Baptist University. The author would like to thank Professor David John Frank for generously sharing the data of international NGOs, together with Dr. Li Chen (the Department of Computer Science at Hong Kong Baptist University), Dr. Lun Zhang (Beijing Normal University), Ms. Mengyi Zhang (Hong Kong Baptist University), and the three anonymous reviewers of the 2017 National Conference of Social Media Processing.; 6th National Conference on Social Media Processing, SMP 2017 ; Conference date: 14-09-2017 Through 17-09-2017",

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doi = "10.1007/978-981-10-6805-8\_25",

language = "English",

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editor = "Xueqi Cheng and Weiying Ma and Huan Liu and Huawei Shen and Shizheng Feng and Xing Xie",

booktitle = "Social Media Processing - 6th National Conference, SMP 2017, Proceedings",

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Zhang, X 2017, Exploring the country co-occurrence network in the twittersphere at an international economic event. in X Cheng, W Ma, H Liu, H Shen, S Feng & X Xie (eds), Social Media Processing - 6th National Conference, SMP 2017, Proceedings. 1st edn, Communications in Computer and Information Science, vol. 774, Springer Verlag, pp. 308-318, 6th National Conference on Social Media Processing, SMP 2017, Beijing, China, 14/09/17. https://doi.org/10.1007/978-981-10-6805-8_25

Exploring the country co-occurrence network in the twittersphere at an international economic event. / Zhang, Xinzhi.
Social Media Processing - 6th National Conference, SMP 2017, Proceedings. ed. / Xueqi Cheng; Weiying Ma; Huan Liu; Huawei Shen; Shizheng Feng; Xing Xie. 1st. ed. Springer Verlag, 2017. p. 308-318 (Communications in Computer and Information Science; Vol. 774).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

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T1 - Exploring the country co-occurrence network in the twittersphere at an international economic event

AU - Zhang, Xinzhi

N1 - Funding Information: Acknowledgments. The study was partly funded by the Start-up Grant for New Academics (no. RC-1617-1-A2) by Hong Kong Baptist University. The author would like to thank Professor David John Frank for generously sharing the data of international NGOs, together with Dr. Li Chen (the Department of Computer Science at Hong Kong Baptist University), Dr. Lun Zhang (Beijing Normal University), Ms. Mengyi Zhang (Hong Kong Baptist University), and the three anonymous reviewers of the 2017 National Conference of Social Media Processing.

PY - 2017/10/24

Y1 - 2017/10/24

N2 - This paper explores how international relations are represented on social media in the context of an international economic event, specifically the “Belt and Road Initiative” proposed by the government of mainland China. The present study focuses on the country co-occurrence network represented in the Twittersphere, such that a link is established between two countries if they appear in the same tweet. The study also investigates how the formation of such a network can be explained by geographical, political, and economic factors. An application programming interface (API) harvested all relevant public tweets (n = 26,515) in a one-month time span (2 June–28 June 2017). The names of the countries or regions were extracted to establish the network, with 52 nodes (countries or regions) and 86 edges. Social network analysis revealed that mainland China, Hong Kong, Pakistan, Greece, Kenya, and Iran were in the network’s important positions, as indicated by their high betweenness centrality. Exponential random graph modeling (ERGM) results suggested that West Asian countries engaging heavily in international polities, countries with lower levels of press freedom, and those receiving less direct investment from mainland China, were more likely to be tweeted together.

AB - This paper explores how international relations are represented on social media in the context of an international economic event, specifically the “Belt and Road Initiative” proposed by the government of mainland China. The present study focuses on the country co-occurrence network represented in the Twittersphere, such that a link is established between two countries if they appear in the same tweet. The study also investigates how the formation of such a network can be explained by geographical, political, and economic factors. An application programming interface (API) harvested all relevant public tweets (n = 26,515) in a one-month time span (2 June–28 June 2017). The names of the countries or regions were extracted to establish the network, with 52 nodes (countries or regions) and 86 edges. Social network analysis revealed that mainland China, Hong Kong, Pakistan, Greece, Kenya, and Iran were in the network’s important positions, as indicated by their high betweenness centrality. Exponential random graph modeling (ERGM) results suggested that West Asian countries engaging heavily in international polities, countries with lower levels of press freedom, and those receiving less direct investment from mainland China, were more likely to be tweeted together.

KW - Country co-occurrence network

KW - Exponential random graph models

KW - International relations

KW - Social network analysis

KW - Text mining

KW - Twittersphere

UR - https://www.scopus.com/pages/publications/85034236205

U2 - 10.1007/978-981-10-6805-8_25

DO - 10.1007/978-981-10-6805-8_25

M3 - Conference proceeding

AN - SCOPUS:85034236205

SN - 9789811068041

T3 - Communications in Computer and Information Science

SP - 308

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BT - Social Media Processing - 6th National Conference, SMP 2017, Proceedings

A2 - Cheng, Xueqi

A2 - Ma, Weiying

A2 - Liu, Huan

A2 - Shen, Huawei

A2 - Feng, Shizheng

A2 - Xie, Xing

PB - Springer Verlag

T2 - 6th National Conference on Social Media Processing, SMP 2017

Y2 - 14 September 2017 through 17 September 2017

ER -

Zhang X. Exploring the country co-occurrence network in the twittersphere at an international economic event. In Cheng X, Ma W, Liu H, Shen H, Feng S, Xie X, editors, Social Media Processing - 6th National Conference, SMP 2017, Proceedings. 1st ed. Springer Verlag. 2017. p. 308-318. (Communications in Computer and Information Science). doi: 10.1007/978-981-10-6805-8_25

Exploring the country co-occurrence network in the twittersphere at an international economic event

Abstract

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