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- Publisher Website: 10.1016/j.im.2018.09.008
- Scopus: eid_2-s2.0-85054456877
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Article: Dissecting emotion and user influence in social media communities: An interaction modeling approach
Title | Dissecting emotion and user influence in social media communities: An interaction modeling approach |
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Authors | |
Keywords | Emotion Network analysis Sentiment analysis Social media analytics Emotion extraction Influence modeling Causal modeling Social computing Border security |
Issue Date | 2020 |
Publisher | Elsevier 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? |
Abstract | 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. |
Description | Special Issue: Big data and business analytics: A research agenda for realizing business value |
Persistent Identifier | http://hdl.handle.net/10722/278766 |
ISSN | 2023 Impact Factor: 8.2 2023 SCImago Journal Rankings: 2.594 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Chung, W | - |
dc.contributor.author | Zeng, D | - |
dc.date.accessioned | 2019-10-21T02:13:40Z | - |
dc.date.available | 2019-10-21T02:13:40Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Information & Management, 2020, v. 57 n. 1, article no. 103108 | - |
dc.identifier.issn | 0378-7206 | - |
dc.identifier.uri | http://hdl.handle.net/10722/278766 | - |
dc.description | Special Issue: Big data and business analytics: A research agenda for realizing business value | - |
dc.description.abstract | 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. | - |
dc.language | eng | - |
dc.publisher | Elsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/im | - |
dc.relation.ispartof | Information & Management | - |
dc.subject | Emotion | - |
dc.subject | Network analysis | - |
dc.subject | Sentiment analysis | - |
dc.subject | Social media analytics | - |
dc.subject | Emotion extraction | - |
dc.subject | Influence modeling | - |
dc.subject | Causal modeling | - |
dc.subject | Social computing | - |
dc.subject | Border security | - |
dc.title | Dissecting emotion and user influence in social media communities: An interaction modeling approach | - |
dc.type | Article | - |
dc.identifier.email | Chung, W: wchun@hku.hk | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.im.2018.09.008 | - |
dc.identifier.scopus | eid_2-s2.0-85054456877 | - |
dc.identifier.hkuros | 307638 | - |
dc.identifier.hkuros | 317381 | - |
dc.identifier.volume | 57 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | article no. 103108 | - |
dc.identifier.epage | article no. 103108 | - |
dc.identifier.isi | WOS:000513292200009 | - |
dc.publisher.place | Netherlands | - |
dc.identifier.issnl | 0378-7206 | - |