The Impact and Reflection of Social Media on Adolescent Cognition and Stereotypes

Authors

  • Miaotian Lu

DOI:

https://doi.org/10.54097/gtt4rc82

Keywords:

Adolescent cognition; social media influence; stereotypes; algorithmic bias; social psychology.

Abstract

The rapid development and popularization of the Internet also have a significant impact on the cognitive development of adolescents. This study combines language theory and cognitive behavioral frameworks to explore how geographic stereotypes affect users' cognitive systems. The paper results found that biased and stereotyped language always exacerbates stereotypes and fixed beliefs. Understanding and intervening in regional label language and overcoming stereotypes is crucial. The root cause of adolescents' susceptibility to such influences lies in their immature cognitive development and weak ability to recognize information influences. At the same time, the group compliance and cultural belonging in the context of Chinese collectivist culture make it easier for them to view regional labels as "universal cognition" and lack reflection. In addition, the strengthening of the Internet algorithm recommendation mechanism and the lack of media literacy education have further amplified the negative impact of stereotypes, which makes it difficult for young people to resist the erosion of tagged discourse in the digital environment. Media literacy education has been proven to be a key intervention pathway. By cultivating teenagers' critical thinking, information filtering ability, and understanding of social narrative constructiveness, it can help them identify algorithmic biases and emotional labels that mislead, break cognitive rigidity, and ultimately guide them to form inclusive cognition of regional diversity. Establish an open cognitive structure and flexible self-identity to maintain cognitive independence and social judgment in the digital age.

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References

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[4] Ma Kunlong. Short term distributed load forecasting method based on big data. Changsha: Hunan University, 2014.

[5] SHI Biao, LI Yu Xia, YU Xhua, YAN Wang. Short-term load forecasting is based on modified particle swarm optimizer and fuzzy neural network models. Systems Engineering-Theory and Practice, 2010, 30(1): 158-160.

[6] Fangfang. Research on power load forecasting based on Improved BP neural network. Harbin Institute of Technology, 2011.

[7] Amjady N. Short-term hourly load forecasting using time series modeling with peak estimation capability. IEEE Transactions on Power Systems, 2001, 16(4): 798-805.

[8] Ma Kunlong. Short term distributed load forecasting method based on big data. Changsha: Hunan University, 2014.

[9] SHI Biao, LI Yu Xia, YU Xhua, YAN Wang. Short-term load forecasting is based on modified particle swarm optimizer and fuzzy neural network models. Systems Engineering-Theory and Practice, 2010, 30(1): 158-160.3

[10] Fangfang. Research on power load forecasting based on Improved BP neural network. Harbin Institute of Technology, 2011.

[11] Amjady N. Short-term hourly load forecasting using time series modeling with peak estimation capability. IEEE Transactions on Power Systems, 2001, 16(4): 798-805.

[12] Ma Kunlong. Short term distributed load forecasting method based on big data. Changsha: Hunan University, 2014.

[13] SHI Biao, LI Yu Xia, YU Xhua, YAN Wang. Short-term load forecasting is based on modified particle swarm optimizer and fuzzy neural network models. Systems Engineering-Theory and Practice, 2010, 30(1): 158-160.

[14] Barranco, K., & Bryant, K. (2025). Media literacy and the future of youth online. PLOS Digital Health, 4(6), e0000876.

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Published

21-11-2025

Issue

Section

Articles

How to Cite

Lu, M. (2025). The Impact and Reflection of Social Media on Adolescent Cognition and Stereotypes. Journal of Education and Educational Research, 15(2), 55-59. https://doi.org/10.54097/gtt4rc82