The Psychological Mechanisms and Legal Regulation of Information Manipulation on Social Media
Research Article
Open Access
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The Psychological Mechanisms and Legal Regulation of Information Manipulation on Social Media

Zijun Nie 1*
1 Jinling High School Hexi Campus
*Corresponding author: 44013818@qq.com
Published on 2 October 2025
Journal Cover
LNEP Vol.123
ISSN (Print): 2753-7056
ISSN (Online): 2753-7048
ISBN (Print): 978-1-80590-401-4
ISBN (Online): 978-1-80590-402-1
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Abstract

In the algorithm-driven landscape of social media, platform manipulation of user cognition and behavior has become increasingly prominent, shaping public opinion and social perception as a critical influencing factor. Based on psychology and law, the paper defines information manipulation, outlines its primary types and psychological mechanisms, and explores how cognitive bias, emotional drive, and social influence jointly contribute to its systemic influence on individual cognition, public opinion, and information security. By integrating literature review with case analysis, it uncovers key regulatory challenges and puts forward a layered governance model that emphasizes platform accountability, technical oversight, and psychological intervention. The results reveal that information manipulation on social media platforms operates through the interplay of algorithmic filtering, emotional amplification, and social influence, consistently altering user cognition and behavior while posing major challenges to public discourse, emotional autonomy, and information security.

Keywords:

Information Manipulation, Social Media, Cognitive Bias, Legal Governance, Psychological Intervention

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Nie,Z. (2025). The Psychological Mechanisms and Legal Regulation of Information Manipulation on Social Media. Lecture Notes in Education Psychology and Public Media,123,50-57.

References

[1]. Thorson, E., & Wells, C. (2016). How gatekeeping still matters: Understanding the role of editors in the digital news era. Journalism Studies, 17(5), 509-527.

[2]. Bakir, V., & McStay, A. (2018). Fake news and the economy of emotions: Problems, causes, solutions. Digital Journalism, 6(2), 154-175.

[3]. Zhang, G. (2020). Cognitive mechanisms of manipulative communication. Journalism & Communication, 25-34.

[4]. Sunstein, C.R. (2017). #Republic: Divided democracy in the age of social media. Princeton University Press.

[5]. Pariser, E. (2011). The filter bubble: What the Internet is hiding from you. New York: Penguin Press.

[6]. Flaxman, S., Goel, S., & Rao, J.M. (2016). Filter bubbles, echo chambers, and online news consumption. Public Opinion Quarterly, 80(S1), 298-320.

[7]. Pennycook, G., & Rand, D. G. (2019). Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning. Cognition, 188, 39-50.

[8]. Tucker, J.A., Guess, A., Barberá, P., et al. (2018). Social media, political polarization, and political disinformation: A review of the scientific literature. Political Science Quarterly, 133(4), 655-688.

[9]. Tufekci, Z. (2015). Algorithmic harms beyond Facebook and Google: Emergent challenges of computational agency. Colorado Technology Law Journal, 13(203), 203-218.

[10]. Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. New York: PublicAffairs.

[11]. Andrejevic, M. (2014). Surveillance and alienation in the online economy. Surveillance & Society, 12(3), 381-397.

[12]. Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.

[13]. Nickerson, R.S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2(2), 175-220.

[14]. Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185, 1124-1131.

[15]. Schwarz, N., et al. (1991). Ease of retrieval as information: Another look at the availability heuristic. Journal of Personality and Social Psychology, 61(2), 195-202.

[16]. Bond, R., & Smith, P.B. (1996). Culture and conformity: A meta-analysis of studies using Asch’s (1952b, 1956) line judgment task. Psychological Bulletin, 119(1), 111-137.

[17]. Lerner, J. S., Gonzalez, R. M., Small, D. A., & Fischhoff, B. (2003). Effects of fear and anger on perceived risks of terrorism: A national field experiment. Psychological Science, 14(2), 144-150.

[18]. Brady, W.J., Wills, J.A., Jost, J.T., et al. (2017). Emotion shapes the diffusion of moralized content in social networks. Proceedings of the National Academy of Sciences, 114(28), 7313-7318.

[19]. Kensinger, E.A., & Schacter, D.L. (2006). Processing emotional pictures and words: Effects of valence and arousal. Cognitive, Affective, & Behavioral Neuroscience, 6(2), 110-126.

[20]. Goldenberg, A., Gross, J.J., & Gal, Y. (2020). Digital emotion contagion. Trends in Cognitive Sciences, 24(4), 316-328.

[21]. Kramer, A.D.I., Guillory, J.E., & Hancock, J.T. (2014). Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences, 111(24), 8788-8790.

[22]. Cinelli, M., Morales, G.D.F., Galeazzi, A., et al. (2021). The echo chamber effect on social media. Proceedings of the National Academy of Sciences, 118(9), e2023301118.

[23]. Papacharissi, Z. (2015). Affective publics: Sentiment, technology, and politics. Oxford University Press.

[24]. European Commission. (2022). The Digital Services Act (DSA). https: //ec.europa.eu/commission/presscorner/detail/en/ip_22_2545

[25]. Richards, N.M., & Raso, F.A. (2023). The future of data governance: Algorithmic accountability and platform regulation. Harvard Journal of Law & Technology, 36(1), 1-45.

[26]. Pasquale, F. (2015). The Black Box Society: The Secret Algorithms That Control Money and Information. Harvard University Press.

[27]. [Lorenz-Spreen, P., Lewandowsky, S., Sunstein, C.R., et al. (2022). How behavioural sciences can promote truth, autonomy and democratic discourse online. Nature Human Behaviour, 6(2), 156-165.

[28]. Helberger, N., Pierson, J., & Poell, T. (2020). Governing online platforms: From contested to cooperative responsibility. The Information Society, 36(1), 1-14.

[29]. Susser, D., Roessler, B., & Nissenbaum, H. (2019). Online manipulation: Hidden influences in a digital world. Georgetown Law Technology Review, 4(1), 1-45.1·

[30]. Kaye, D. (2021). Social media regulation: Problems and solutions. European Journal of International Law, 32(2), 583-598.

References

[1]. Thorson, E., & Wells, C. (2016). How gatekeeping still matters: Understanding the role of editors in the digital news era. Journalism Studies, 17(5), 509-527.

[2]. Bakir, V., & McStay, A. (2018). Fake news and the economy of emotions: Problems, causes, solutions. Digital Journalism, 6(2), 154-175.

[3]. Zhang, G. (2020). Cognitive mechanisms of manipulative communication. Journalism & Communication, 25-34.

[4]. Sunstein, C.R. (2017). #Republic: Divided democracy in the age of social media. Princeton University Press.

[5]. Pariser, E. (2011). The filter bubble: What the Internet is hiding from you. New York: Penguin Press.

[6]. Flaxman, S., Goel, S., & Rao, J.M. (2016). Filter bubbles, echo chambers, and online news consumption. Public Opinion Quarterly, 80(S1), 298-320.

[7]. Pennycook, G., & Rand, D. G. (2019). Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning. Cognition, 188, 39-50.

[8]. Tucker, J.A., Guess, A., Barberá, P., et al. (2018). Social media, political polarization, and political disinformation: A review of the scientific literature. Political Science Quarterly, 133(4), 655-688.

[9]. Tufekci, Z. (2015). Algorithmic harms beyond Facebook and Google: Emergent challenges of computational agency. Colorado Technology Law Journal, 13(203), 203-218.

[10]. Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. New York: PublicAffairs.

[11]. Andrejevic, M. (2014). Surveillance and alienation in the online economy. Surveillance & Society, 12(3), 381-397.

[12]. Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.

[13]. Nickerson, R.S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2(2), 175-220.

[14]. Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185, 1124-1131.

[15]. Schwarz, N., et al. (1991). Ease of retrieval as information: Another look at the availability heuristic. Journal of Personality and Social Psychology, 61(2), 195-202.

[16]. Bond, R., & Smith, P.B. (1996). Culture and conformity: A meta-analysis of studies using Asch’s (1952b, 1956) line judgment task. Psychological Bulletin, 119(1), 111-137.

[17]. Lerner, J. S., Gonzalez, R. M., Small, D. A., & Fischhoff, B. (2003). Effects of fear and anger on perceived risks of terrorism: A national field experiment. Psychological Science, 14(2), 144-150.

[18]. Brady, W.J., Wills, J.A., Jost, J.T., et al. (2017). Emotion shapes the diffusion of moralized content in social networks. Proceedings of the National Academy of Sciences, 114(28), 7313-7318.

[19]. Kensinger, E.A., & Schacter, D.L. (2006). Processing emotional pictures and words: Effects of valence and arousal. Cognitive, Affective, & Behavioral Neuroscience, 6(2), 110-126.

[20]. Goldenberg, A., Gross, J.J., & Gal, Y. (2020). Digital emotion contagion. Trends in Cognitive Sciences, 24(4), 316-328.

[21]. Kramer, A.D.I., Guillory, J.E., & Hancock, J.T. (2014). Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences, 111(24), 8788-8790.

[22]. Cinelli, M., Morales, G.D.F., Galeazzi, A., et al. (2021). The echo chamber effect on social media. Proceedings of the National Academy of Sciences, 118(9), e2023301118.

[23]. Papacharissi, Z. (2015). Affective publics: Sentiment, technology, and politics. Oxford University Press.

[24]. European Commission. (2022). The Digital Services Act (DSA). https: //ec.europa.eu/commission/presscorner/detail/en/ip_22_2545

[25]. Richards, N.M., & Raso, F.A. (2023). The future of data governance: Algorithmic accountability and platform regulation. Harvard Journal of Law & Technology, 36(1), 1-45.

[26]. Pasquale, F. (2015). The Black Box Society: The Secret Algorithms That Control Money and Information. Harvard University Press.

[27]. [Lorenz-Spreen, P., Lewandowsky, S., Sunstein, C.R., et al. (2022). How behavioural sciences can promote truth, autonomy and democratic discourse online. Nature Human Behaviour, 6(2), 156-165.

[28]. Helberger, N., Pierson, J., & Poell, T. (2020). Governing online platforms: From contested to cooperative responsibility. The Information Society, 36(1), 1-14.

[29]. Susser, D., Roessler, B., & Nissenbaum, H. (2019). Online manipulation: Hidden influences in a digital world. Georgetown Law Technology Review, 4(1), 1-45.1·

[30]. Kaye, D. (2021). Social media regulation: Problems and solutions. European Journal of International Law, 32(2), 583-598.

Cite this article

Nie,Z. (2025). The Psychological Mechanisms and Legal Regulation of Information Manipulation on Social Media. Lecture Notes in Education Psychology and Public Media,123,50-57.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of ICILLP 2025 Symposium: Psychological Perspectives on Teacher-Student Relationships in Educational Contexts

ISBN: 978-1-80590-401-4(Print) / 978-1-80590-402-1(Online)
Editor: Renuka Thakore, Abdullah Laghari
Conference website: https://2025.icillp.org/Nawabshah.html
Conference date: 17 October 2025
Series: Lecture Notes in Education Psychology and Public Media
Volume number: Vol.123
ISSN: 2753-7048(Print) / 2753-7056(Online)

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