Dissecting emotion and user influence in social media communities: An interaction modeling approach

doi:10.1016/j.im.2018.09.008

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 互联网大数据与信息安全

	Dissecting emotion and user influence in social media communities: An interaction modeling approach
	Chung, Wingyan 1; Zeng, Daniel2,3
发表期刊	INFORMATION & MANAGEMENT
ISSN	0378-7206
	2020
卷号	57 期号:1 页码:16
通讯作者	Chung, Wingyan(wchung@ucf.edu)
摘要	Human emotion expressed in social media plays an increasingly important role in shaping policies and decisions. However, the process by which emotion produces influence in online social media networks is relatively unknown. Previous works focus largely on sentiment classification and polarity identification but do not adequately consider the way emotion affects user influence. This research developed a novel framework, a theory-based model, and a proof-of-concept system for dissecting emotion and user influence in social media networks. The system models emotion-triggered influence and facilitates analysis of emotion-influence causality in the context of U.S. border security (using 5,327,813 tweets posted by 1,303,477 users). Motivated by a theory of emotion spread, the model was integrated in an influence-computation method, called the interaction modeling (IM) approach, which was compared with a benchmark using a user centrality (UC) approach based on social positions. IM was found to have identified influential users who are more broadly related to U.S. cultural issues. Influential users tended to express intense emotions of fear, anger, disgust, and sadness. The emotion trust distinguishes influential users from others, whereas anger and fear contributed significantly to causing user influence. The research contributes to incorporating human emotion into the data-information-knowledge-wisdom model of knowledge management and to providing new information systems artifacts and new causality findings for emotion-influence analysis.
关键词	Emotion Network analysis Sentiment analysis Social media analytics Emotion extraction Influence modeling Causal modeling Social computing Border security
DOI	10.1016/j.im.2018.09.008
关键词[WOS]	BUSINESS INTELLIGENCE ; DECISION-MAKING ; SENTIMENT ; DIFFUSION ; ANALYTICS ; FRAMEWORK ; NETWORKS
收录类别	SCI
语种	英语
资助项目	U.S. Defense Advanced Research Projects Agency[FA8650-18-C-7824] ; Florida Center for Cybersecurity[2108-1106-00-G] ; National Natural Science Foundation of China[71621002] ; Chinese Academy of Sciences[ZDRW-XH-2017-3] ; U.S. Defense Advanced Research Projects Agency[FA8650-18-C-7824] ; Florida Center for Cybersecurity[2108-1106-00-G] ; National Natural Science Foundation of China[71621002] ; Chinese Academy of Sciences[ZDRW-XH-2017-3]
项目资助者	U.S. Defense Advanced Research Projects Agency ; Florida Center for Cybersecurity ; National Natural Science Foundation of China ; Chinese Academy of Sciences
WOS研究方向	Computer Science ; Information Science & Library Science ; Business & Economics
WOS类目	Computer Science, Information Systems ; Information Science & Library Science ; Management
WOS记录号	WOS:000513292200009
出版者	ELSEVIER
引用统计	被引频次：51[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/38506
专题	多模态人工智能系统全国重点实验室_互联网大数据与信息安全
通讯作者	Chung, Wingyan
作者单位	1.Univ Cent Florida, Inst Simulat & Training, 3100 Technol Pkwy, Orlando, FL 32826 USA 2.Univ Arizona, Eller Coll Management, Dept Management Informat Syst, 1130 E Helen St, Tucson, AZ 85721 USA 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Chung, Wingyan,Zeng, Daniel. Dissecting emotion and user influence in social media communities: An interaction modeling approach[J]. INFORMATION & MANAGEMENT,2020,57(1):16.
APA	Chung, Wingyan,&Zeng, Daniel.(2020).Dissecting emotion and user influence in social media communities: An interaction modeling approach.INFORMATION & MANAGEMENT,57(1),16.
MLA	Chung, Wingyan,et al."Dissecting emotion and user influence in social media communities: An interaction modeling approach".INFORMATION & MANAGEMENT 57.1(2020):16.