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

TitleDissecting emotion and user influence in social media communities: An interaction modeling approach
Authors
KeywordsEmotion
Network analysis
Sentiment analysis
Social media analytics
Emotion extraction
Influence modeling
Causal modeling
Social computing
Border security
Issue Date2020
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/im
Citation
Information & Management, 2020, v. 57 n. 1, article no. 103108 How to Cite?
AbstractHuman 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.
Persistent Identifierhttp://hdl.handle.net/10722/278766
ISSN
2017 Impact Factor: 3.89
2015 SCImago Journal Rankings: 1.381

 

DC FieldValueLanguage
dc.contributor.authorChung, W-
dc.contributor.authorZeng, D-
dc.date.accessioned2019-10-21T02:13:40Z-
dc.date.available2019-10-21T02:13:40Z-
dc.date.issued2020-
dc.identifier.citationInformation & Management, 2020, v. 57 n. 1, article no. 103108-
dc.identifier.issn0378-7206-
dc.identifier.urihttp://hdl.handle.net/10722/278766-
dc.description.abstractHuman 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.-
dc.languageeng-
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/im-
dc.relation.ispartofInformation & Management-
dc.subjectEmotion-
dc.subjectNetwork analysis-
dc.subjectSentiment analysis-
dc.subjectSocial media analytics-
dc.subjectEmotion extraction-
dc.subjectInfluence modeling-
dc.subjectCausal modeling-
dc.subjectSocial computing-
dc.subjectBorder security-
dc.titleDissecting emotion and user influence in social media communities: An interaction modeling approach-
dc.typeArticle-
dc.identifier.emailChung, W: wchun@hku.hk-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.im.2018.09.008-
dc.identifier.scopuseid_2-s2.0-85054456877-
dc.identifier.hkuros307638-
dc.identifier.volume57-
dc.identifier.issue1-
dc.identifier.spagearticle no. 103108-
dc.identifier.epagearticle no. 103108-
dc.publisher.placeNetherlands-

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