IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v490y2018icp185-202.html
   My bibliography  Save this article

An emotional contagion model for heterogeneous social media with multiple behaviors

Author

Listed:
  • Xiong, Xi
  • Li, Yuanyuan
  • Qiao, Shaojie
  • Han, Nan
  • Wu, Yue
  • Peng, Jing
  • Li, Binyong

Abstract

The emotion varies and propagates with the spatial and temporal information of individuals through social media, which uncovers several interaction mechanisms and features the community structure in order to facilitate individuals’ communication and emotional contagion in social networks. Aiming to show the detailed process and characteristics of emotional contagion within social media, we propose an emotional independent cascade model in which individual emotion can affect the subsequent emotion of his/her friends. The transmissibility is introduced to measure the capability of propagating emotion with respect to an individual in social networks. By analyzing the patterns of emotional contagion on Twitter data, we find that the value of transmissibility differs on different layers and on different community structures. Extensive experiments were conducted and the results reveal that, the polar emotion of hub users can lead to the disappearance of opposite emotion, and the transmissibility makes no sense. The final emotional distribution depends on the initial emotional distribution and the transmissibilities. Individuals from a small community are more likely to change their mood by the influence of community leaders. In addition, we compared the proposed model with two other models, the emotion-based spreader–ignorant–stifler model and the standard independent cascade model. The results demonstrate that the proposed model can reflect the real-world situation of emotional contagion for heterogeneous social media while the computational complexities of all these three models are similar.

Suggested Citation

  • Xiong, Xi & Li, Yuanyuan & Qiao, Shaojie & Han, Nan & Wu, Yue & Peng, Jing & Li, Binyong, 2018. "An emotional contagion model for heterogeneous social media with multiple behaviors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 185-202.
    • Handle: RePEc:eee:phsmap:v:490:y:2018:i:c:p:185-202
    DOI: 10.1016/j.physa.2017.08.025
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437117307495
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2017.08.025?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mike Thelwall & Kevan Buckley, 2013. "Topic-based sentiment analysis for the social web: The role of mood and issue-related words," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(8), pages 1608-1617, August.
    2. Borondo, J. & Morales, A.J. & Benito, R.M. & Losada, J.C., 2015. "Multiple leaders on a multilayer social media," Chaos, Solitons & Fractals, Elsevier, vol. 72(C), pages 90-98.
    3. Emilio Ferrara & Zeyao Yang, 2015. "Measuring Emotional Contagion in Social Media," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-14, November.
    4. Mike Thelwall & Kevan Buckley & Georgios Paltoglou, 2012. "Sentiment strength detection for the social web," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(1), pages 163-173, January.
    5. Mike Thelwall & Kevan Buckley & Georgios Paltoglou, 2012. "Sentiment strength detection for the social web," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(1), pages 163-173, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shao, Quan & Wang, Hong & Zhu, Pei & Dong, Min, 2021. "Group emotional contagion and simulation in large-scale flight delays based on the two-layer network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    2. Yao, Yao & Li, Yuanyuan & Xiong, Xi & Wu, Yue & Lin, Honggang & Ju, Shenggen, 2020. "An interactive propagation model of multiple information in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    3. Peihua Fu & Bailu Jing & Tinggui Chen & Jianjun Yang & Guodong Cong, 2020. "Modeling Network Public Opinion Propagation with the Consideration of Individual Emotions," IJERPH, MDPI, vol. 17(18), pages 1-29, September.
    4. Xiao, Yunpeng & Zhang, Li & Li, Qian & Liu, Ling, 2019. "MM-SIS: Model for multiple information spreading in multiplex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 135-146.
    5. Chołoniewski, Jan & Sienkiewicz, Julian & Leban, Gregor & Hołyst, Janusz A., 2019. "Modeling of temporal fluctuation scaling in online news network with independent cascade model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 129-144.
    6. Zhai, Xueting & Zhong, Dixi & Luo, Qiuju, 2019. "Turn it around in crisis communication: An ABM approach," Annals of Tourism Research, Elsevier, vol. 79(C).
    7. Hainan Huang & Weifan Chen & Tian Xie & Yaoyao Wei & Ziqing Feng & Weijiong Wu, 2021. "The Impact of Individual Behaviors and Governmental Guidance Measures on Pandemic-Triggered Public Sentiment Based on System Dynamics and Cross-Validation," IJERPH, MDPI, vol. 18(8), pages 1-25, April.
    8. Meng, Yanhong & Yi, Yunhui & Xiong, Fei & Pei, Changxing, 2019. "T×oneHop approach for dynamic influence maximization problem," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 575-586.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Martin Haselmayer & Marcelo Jenny, 2017. "Sentiment analysis of political communication: combining a dictionary approach with crowdcoding," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(6), pages 2623-2646, November.
    2. Gang Wang & Daqing Zheng & Shanlin Yang & Jian Ma, 2018. "FCE-SVM: a new cluster based ensemble method for opinion mining from social media," Information Systems and e-Business Management, Springer, vol. 16(4), pages 721-742, November.
    3. Agrawal, Shiv Ratan & Mittal, Divya, 2022. "Optimizing customer engagement content strategy in retail and E-tail: Available on online product review videos," Journal of Retailing and Consumer Services, Elsevier, vol. 67(C).
    4. Young Bin Kim & Sang Hyeok Lee & Shin Jin Kang & Myung Jin Choi & Jung Lee & Chang Hun Kim, 2015. "Virtual World Currency Value Fluctuation Prediction System Based on User Sentiment Analysis," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-18, August.
    5. Booranakittipinyo, Amphai & Li, Rita Yi Man & Phakdeephirot, Nutteera, 2024. "Travelers' perception of smart airport facilities: An X (Twitter) sentiment analysis," Journal of Air Transport Management, Elsevier, vol. 118(C).
    6. Singh, Amit & Jenamani, Mamata & Thakkar, Jitesh J. & Rana, Nripendra P., 2022. "Quantifying the effect of eWOM embedded consumer perceptions on sales: An integrated aspect-level sentiment analysis and panel data modeling approach," Journal of Business Research, Elsevier, vol. 138(C), pages 52-64.
    7. Ping-Yu Hsu & Hong-Tsuen Lei & Shih-Hsiang Huang & Teng Hao Liao & Yao-Chung Lo & Chin-Chun Lo, 2019. "Effects of sentiment on recommendations in social network," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(2), pages 253-262, June.
    8. Fatma Najar & Nizar Bouguila, 2023. "On smoothing and scaling language model for sentiment based information retrieval," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(3), pages 725-744, September.
    9. Luis J. Callarisa-Fiol & Miguel Ángel Moliner-Tena & Rosa Rodríguez-Artola & Javier Sánchez-García, 2023. "Entrepreneurship innovation using social robots in tourism: a social listening study," Review of Managerial Science, Springer, vol. 17(8), pages 2945-2971, November.
    10. Phuong Nguyen Hong Huynh & Tin Trung Hoang & Huynh Thi Thuy Phan & Quynh Le Nhu Nguyen, 2024. "The customers’ perception of privacy in the retail industry when adopting digital transformation," HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE - ECONOMICS AND BUSINESS ADMINISTRATION, HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE, HO CHI MINH CITY OPEN UNIVERSITY, vol. 14(2), pages 143-159.
    11. Jifeng Mu & Jonathan Z. Zhang, 2021. "Seller marketing capability, brand reputation, and consumer journeys on e-commerce platforms," Journal of the Academy of Marketing Science, Springer, vol. 49(5), pages 994-1020, September.
    12. Annamalai, Balamurugan & Yoshida, Masayuki & Varshney, Sanjeev & Pathak, Atul Arun & Venugopal, Pingali, 2021. "Social media content strategy for sport clubs to drive fan engagement," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).
    13. P. D. Mahendhiran & S. Kannimuthu, 2018. "Deep Learning Techniques for Polarity Classification in Multimodal Sentiment Analysis," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(03), pages 883-910, May.
    14. Simon Albrecht & Bernhard Lutz & Dirk Neumann, 2020. "The behavior of blockchain ventures on Twitter as a determinant for funding success," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(2), pages 241-257, June.
    15. Yankang Su & Zbigniew J. Kabala, 2023. "Public Perception of ChatGPT and Transfer Learning for Tweets Sentiment Analysis Using Wolfram Mathematica," Data, MDPI, vol. 8(12), pages 1-27, November.
    16. Yawar Abbas & M. S. I. Malik, 2023. "Defective products identification framework using online reviews," Electronic Commerce Research, Springer, vol. 23(2), pages 899-920, June.
    17. Li, Xinwei & Xu, Mao & Zeng, Wenjuan & Tse, Ying Kei & Chan, Hing Kai, 2023. "Exploring customer concerns on service quality under the COVID-19 crisis: A social media analytics study from the retail industry," Journal of Retailing and Consumer Services, Elsevier, vol. 70(C).
    18. Kristina Lerman & Luciano G. Marin & Megha Arora & Lucas H. Costa Lima & Emilio Ferrara & David Garcia, 2018. "Language, demographics, emotions, and the structure of online social networks," Journal of Computational Social Science, Springer, vol. 1(1), pages 209-225, January.
    19. Ghasem Javadi & Mohammad Taleai, 2020. "Integration of User Generated Geo-contents and Official Data to Assess Quality of Life in Intra-national Level," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 152(1), pages 205-235, November.
    20. José Luis Ortega, 2022. "Classification and analysis of PubPeer comments: How a web journal club is used," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(5), pages 655-670, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:490:y:2018:i:c:p:185-202. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.