2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 18 , Issue 2 , PP: 239-257, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Prapti Pandey 1 , Vivek Shukla 2 , Rohit Miri 3 , Praveen Chouksey 4 , Parul Dubey 5 , Rohit Raja 6
Doi: https://doi.org/10.54216/JISIoT.180217
Natural Language Processing (NLP) and Network Science were combined to study emotional contagion dynamics in social media networks. We simulated the diffusion of emotions through users on a synthetic interaction network using sentiment-labeled Twitter data and a graph-based model. We explored the relationship between graph metrics, including centrality and clustering coefficient, on emotion propagation and stability. The findings show that emotion intensity converges through the network and that both weak coupling of central nodes and moderate cluster structures dampen the spread of emotion. A community-level analysis reveals more alternative results, such as the fact that emotions differ in polarity between communities. Our work demonstrates a better understanding of how emotional behavior in online environments can be adjusted using semantic measures, which offer a means to characterize the relevance of information online and the interconnected relationships among emotionality.
Emotional Contagion , Sentiment Analysis , Natural Language Processing (NLP) , Network Science , Social Media , Clustering Coefficient , Emotion Diffusion
[1] M. Chu, W. Song, Z. Zhao, T. Chen and Y. Chiang, “Emotional contagion on social media and the simulation of intervention strategies after a disaster event: a modeling study,” Humanities and Social Sciences Communications, vol. 11, p. 968, 2024.
[2] M. K. Alshahrani, S. A. Alshahrani, and S. M. Alshammari, “Understanding Emotional Contagion in Social Media: A Study of Twitter,” Journal of Social Media Studies, vol. 5, no. 1, pp. 45–60, 2023, doi: 10.1016/j.jsms.2023.01.007.
[3] T. Wang, L. Zhang, and Y. Wang, “Emotional Dynamics in Online Social Networks: A Machine Learning Approach,” Journal of Computational Social Science, vol. 7, no. 2, pp. 215–230, 2024, doi: 10.1007/s42001-024-00123-4.
[4] Y. Zhang and X. Wang, “Emotional contagion-based social sentiment mining in social networks by introducing network communities,” ACM Trans. Knowl. Discov. Data (TKDD), vol. 13, no. 6, pp. 1–22, 2023.
[5] J. Tang, X. Wang, and Y. Liu, “Emotional contagion in the online depression community,” ResearchGate, 2021.
[6] C. Gao, X. Lan, Z. Lu, J. Mao, J. Piao, H. Wang, D. Jin, and Y. Li, “S³: Social-network Simulation System with Large Language Model-Empowered Agents,” arXiv preprint, arXiv:2307.14984, 2023.
[7] J. Ren, T. Fan, L. Lü, and X. Fu, “Information cocooning as a group emotional stabilization role in social networks,” arXiv preprint, arXiv: 2408.16295, 2023.
[8] A. R. Khosravi and F. A. Shafiei, “The Role of Emotions in Social Media Interactions: A Systematic Review,” International Journal of Information Management, vol. 68, pp. 102-112, 2023, doi: 10.1016/j.ijinfomgt.2023.102112.
[9] E. Ferrara and Z. Yang, “Measuring emotional contagion in social media,” PLOS ONE, vol. 10, no. 11, p. e0142390, 2015.
[10] R. Fan, K. Xu, and J. Zhao, “Higher contagion and weaker ties mean anger spreads faster than joy in social media,” arXiv preprint arXiv: 1608.03656, 2016.
[11] M. Del Vicario et al., “Echo chambers: Emotional contagion and group polarization on Facebook,” arXiv preprint arXiv: 1607.01032, 2016.
[12] X. Xiong, Y. Liu, and L. Wang, “An emotional contagion model for heterogeneous social media with multiple behaviors,” Physica A: Statistical Mechanics and its Applications, vol. 479, pp. 120–130, 2017.
[13] L. Lin, Y. Wang, and Z. Liu, “Revealing the sentiment propagation under the conscious emotional contagion mechanism in the social media ecosystem,” Neurocomputing, vol. 423, pp. 1–12, 2021.
[14] P. Lv et al., “Emotional contagion-aware deep reinforcement learning for antagonistic crowd simulation,” arXiv preprint arXiv: 2105.00854, 2021.
[15] Y. Liu, J. Li, X. Wang, and Z. Zeng, “EmotionIC: Emotional inertia and contagion-driven dependency modeling for emotion recognition in conversation,” arXiv preprint arXiv: 2303.11117, 2023.
[16] H. Li and J. Chen, “Measuring emotional wellbeing and emotional contagion through social media: A computational approach,” SAGE Open, vol. 14, no. 1, pp. 1–12, 2024.
[17] J. Tang, X. Wang, and Y. Liu, “Emotional contagion in the online depression community,” Frontiers in Psychology, vol. 12, p. 712606, 2021.
[18] R. A. Adeeb and M. Mirhoseini, “The impact of affect on the perception of fake news on social media: A systematic review,” Social Sciences, vol. 12, no. 12, p. 674, 2023.
[19] C. Gao et al., “S³: Social-network simulation system with large language model-empowered agents,” arXiv preprint arXiv: 2307.14984, 2023.
[20] J. Ren, T. Fan, L. Lü, and X. Fu, “Information cocooning as a group emotional stabilization role in social networks,” arXiv preprint arXiv: 2408.16295, 2023.
[21] Y. Zhang and X. Wang, “Emotional contagion-based social sentiment mining in social networks by introducing network communities,” ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 13, no. 6, pp. 1–22, 2019.
[22] M. Chu et al., “Emotional contagion on social media and the simulation of intervention strategies after a disaster event: A modeling study,” Humanities and Social Sciences Communications, vol. 11, p. 968, 2024.
[23] J. Li et al., “Exploring emotional contagion and reciprocity between social media influencers and followers: A content analysis of autobiographic content about depression and comments on Douyin,” Mass Communication and Society, 2025.
