A discussion on social media addiction from the perspective of social psychology in the relationship between college students and teachers based on biological evolution models
Abstract
This study explores the biomechanical mechanisms of social media addiction, with a particular focus on its long-term effects on brain function and hand muscle control. By combining neurobiological and biomechanical models, this article analyzes how social media use enhances user dependency by activating the brain’s reward system, particularly the dopamine system, and leads to muscle fatigue and precision adaptation through repeated hand movements such as sliding and clicking. The dopamine release model we proposed reveals temporal changes in dopamine during social media interactions, further influencing users’ behavioral patterns and self-control abilities. Based on the muscle fatigue model, we demonstrate the adaptation process of hand muscles during continuous repetitive operations, resulting in improved hand accuracy but also accelerating the accumulation of fatigue. In addition, the prefrontal cortex activity model suggests that long-term social media use may weaken an individual’s impulse regulation function by reducing self-control. To verify these biomechanical effects, we have demonstrated through experiments that the SVD recommendation algorithm exhibits significant advantages over traditional recommendation algorithms in improving operational accuracy, reducing reaction time, and alleviating muscle fatigue. The experimental results show that the SVD model not only improves the accuracy of the recommendation system, but also optimizes the interaction experience between users and the platform, effectively reducing the biomechanical and cognitive burden.
References
1. Koç H, Şimşir Gökalp Z, Seki T. The Relationships Between Self-Control and Distress Among Emerging Adults: A Serial Mediating Roles of Fear of Missing Out and Social Media Addiction. Emerging Adulthood. 2023; 11(3): 626-638. doi: 10.1177/21676968231151776
2. Lu X, Zhang M, Zhang J. The relationship between social support and Internet addiction among Chinese college freshmen: A mediated moderation model. Frontiers in Psychology. 2023; 13. doi: 10.3389/fpsyg.2022.1031566
3. Roy D, Dutta M. A systematic review and research perspective on recommender systems. Journal of Big Data. 2022; 9(1). doi: 10.1186/s40537-022-00592-5
4. Liang B, Sha C, Wu D, et al. ACCF: Learning Attentional Conformity for Collaborative Filtering. IEEE Access. 2019; 7: 148541-148549. doi: 10.1109/access.2019.2921853
5. Al-Obeidat F, Rocha Á, Khan MS, et al. Parallel tensor factorization for relational learning. Neural Computing and Applications. 2021; 34(11): 8455-8464. doi: 10.1007/s00521-021-05692-6
6. Tian Z, Liu Y, Sun J, et al. Exploiting Group Information for Personalized Recommendation with Graph Neural Networks. ACM Transactions on Information Systems. 2021; 40(2): 1-23. doi: 10.1145/3464764
7. Wang Q, Chen H, Hu W, et al. Social networking sites addiction and depression among Chinese college students: The mediating role of cognitive flexibility and the moderating role of chronotype. Children and Youth Services Review. 2023; 155: 107209. doi: 10.1016/j.childyouth.2023.107209
8. Kulkarni S, Rodd SF. Context Aware Recommendation Systems: A review of the state of the art techniques. Computer Science Review. 2020; 37: 100255. doi: 10.1016/j.cosrev.2020.100255
9. Bergner Y, Halpin P, Vie JJ. Multidimensional Item Response Theory in the Style of Collaborative Filtering. Psychometrika. 2022; 87(1): 266-288. doi: 10.1007/s11336-021-09788-9
10. Hamid RA, Albahri AS, Alwan JK, et al. How smart is e-tourism? A systematic review of smart tourism recommendation system applying data management. Computer Science Review. 2021; 39: 100337. doi: 10.1016/j.cosrev.2020.100337
11. Maltby J, Rayes T, Nage A, et al. Synthesizing perspectives: Crafting an Interdisciplinary view of social media’s impact on young people’s mental health. PLOS ONE. 2024; 19(7): e0307164. doi: 10.1371/journal.pone.0307164
12. Hassanpour B, Abdolvand N, & Rajaee Harandi S. Improving Accuracy of Recommender Systems using Social Network Information and Longitudinal Data. Journal of AI and Data Mining. 2020; 8(3): 379-389. doi: 10.22044/jadm.2020.7326.1871
13. Wei XY, Liang HY, Gao T, et al. Preference for Smartphone-Based Internet Applications and Smartphone Addiction Among Young Adult Addicts: Gender Difference in Psychological Network. Social Science Computer Review. 2023; 42(5): 1266-1281. doi: 10.1177/08944393231222680
14. Cui C. Full-size Computer Simulation Model Design of 3D Braided Composites. Mari Papel Y Corrugado. 2024; 2024(1): 98–105.
15. Chakraborty K, Bhattacharyya S, Bag R. A Survey of Sentiment Analysis from Social Media Data. IEEE Transactions on Computational Social Systems. 2020; 7(2): 450-464. doi: 10.1109/tcss.2019.2956957
16. Zhou B, Mui LG, Li J, et al. A model for risk factors harms and of smartphone addiction among nursing students: A scoping review. Nurse Education in Practice. 2024; 75: 103874. doi: 10.1016/j.nepr.2024.103874
17. Saha K, Grover T, Mattingly SM, et al. Person-Centered Predictions of Psychological Constructs with Social Media Contextualized by Multimodal Sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 2021; 5(1): 1-32. doi: 10.1145/3448117
18. Hassan F, Behadili SF. Modeling Social Networks using Data Mining Approaches-Review. Iraqi Journal of Science. Published online March 30, 2022: 1313-1338. doi: 10.24996/ijs.2022.63.3.35
19. Tülübaş T, Karakose T, Papadakis S. A Holistic Investigation of the Relationship between Digital Addiction and Academic Achievement among Students. European Journal of Investigation in Health, Psychology and Education. 2023; 13(10): 2006-2034. doi: 10.3390/ejihpe13100143
20. Kim HS, Yang S, Kim M, et al. An Experimental Study of Recommendation Algorithms for Tailored Health Communication. Computational Communication Research. 2019; 1(1): 103-129. doi: 10.5117/ccr2019.1.005.sukk
21. Zamini M, Hasheminejad SMH. A comprehensive survey of anomaly detection in banking, wireless sensor networks, social networks, and healthcare. Intelligent Decision Technologies. 2019; 13(2): 229-270. doi: 10.3233/idt-170155
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