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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
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    References listed on IDEAS

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    1. Emilio Ferrara & Zeyao Yang, 2015. "Measuring Emotional Contagion in Social Media," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-14, November.
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    Citations

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    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.

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