TY - GEN
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
Y1 - 2017
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 - http://www.scopus.com/inward/record.url?scp=85034236205&partnerID=8YFLogxK
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
EP - 318
BT - Social Media Processing - 6th National Conference, SMP 2017, Proceedings
A2 - Liu, Huan
A2 - Xie, Xing
A2 - Cheng, Xueqi
A2 - Shen, Huawei
A2 - Ma, Weiying
A2 - Feng, Shizheng
PB - Springer Verlag
T2 - 6th National Conference on Social Media Processing, SMP 2017
Y2 - 14 September 2017 through 17 September 2017
ER -
