The Dynamics of Misinformation Spread on Social Media Networks

Authors

  • Chunhan Liu

DOI:

https://doi.org/10.54691/6gpycc30

Keywords:

Youth career development, structural shame, occupational stereotypes, moral cognition, emotional socialization.

Abstract

In today's era, where digital communication technologies are significantly transforming the way information is shared, social media—leveraging its exceptional interactivity and dissemination efficiency—has emerged as the central platform for information exchange. Nevertheless, this development is accompanied by a critical challenge: the widespread proliferation of misinformation. This paper provides a thorough and systematic review of research advancements in misinformation dissemination on social media. It begins by examining both classical and state-of-the-art information dissemination models, investigates the distinctive mechanisms through which platforms amplify content, analyzes the impact of human behavior and cognitive factors, and evaluates existing strategies for detection and mitigation. The findings indicate that while some progress has been made in addressing misinformation through technological innovations and intervention measures, several obstacles remain. These include privacy protection concerns, difficulties in managing cross-platform dissemination, and the growing threat posed by adversarial attacks, all of which hinder the overall effectiveness of governance efforts.

Downloads

Download data is not yet available.

References

[1] Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2017). Fake news detection on social media: A data mining perspective. ACM SIGKDD Explorations Newsletter, 19(1), 22–36. https://doi.org/10.1145/3137597.3137600

[2] Cinelli, M., Quattrociocchi, W., Galeazzi, A., et al. (2020). The COVID-19 social media infodemic. Scientific Reports, 10(1), 16598. https://doi.org/10.1038/s41598-020-73510-5

[3] Bakshy, E., Messing, S., & Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on Facebook. Science, 348(6239), 1130–1132. https://doi.org/10.1126/science.aaa1160

[4] Zannettou, S., Sirivianos, M., Blackburn, J., & Kourtellis, N. (2019). The web centipede: Understanding how web communities influence each other through the lens of mainstream and alternative news sources. Proceedings of the 2019 Internet Measurement Conference, 405–417. https://doi.org/10.1145/3131365.3131390

[5] Pennycook, G., & Rand, D. G. (2020). Fighting misinformation on social media using crowdsourced judgments of news source quality. Proceedings of the National Academy of Sciences, 117(6), 2771–2776.

[6] Barabási, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512. https://doi.org/10.1126/science.286.5439.509

[7] Garimella, K., Morales, G. D. F., Gionis, A., & Mathioudakis, M. (2018). Quantifying controversy in social media. ACM Transactions on Social Computing, 1(1), 1–27.

[8] Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2(2), 175–220. https://psycnet.apa.org/doi/10.1037/1089-2680.2.2.175

[9] Berger, J., & Milkman, K. L. (2012). What makes online content viral? Journal of Marketing Research, 49(2), 192–205.

[10] Kempe, D., Kleinberg, J., & Tardos, É. (2003). Maximizing the spread of influence through a social network. Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 137–146.

[11] Monti, F., Frasca, F., Eynard, D., Mannion, D., & Bronstein, M. M. (2019). Fake news detection on social media using geometric deep learning.

[12] Ma, J., Gao, W., & Wong, K. F. (2016). Detect rumors in microblog posts using propagation structure via kernel learning. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL).

[13] Zhou, X., & Zafarani, R. (2020). A survey of fake news: Fundamental theories, detection methods, and opportunities. ACM Computing Surveys, 53(5), 1–40.

[14] Shao, C., Ciampaglia, G. L., Varol, O., Flammini, A., & Menczer, F. (2018). The spread of low-credibility content by social bots. Nature Communications, 9(1), 4787.

[15] Roozenbeek, J., van der Linden, S., & Nygren, T. (2020). Prebunking interventions based on “inoculation” theory reduce susceptibility to misinformation across cultures. Harvard Kennedy School Misinformation Review, 1(2).

[16] Guess, A., Lerner, M., Lyons, B., Montgomery, J. M., Nyhan, B., & Reifler, J. (2020). A digital media literacy intervention increases discernment between mainstream and false news in the United States and India. Proceedings of the National Academy of Sciences, 117(27), 15536–15545.

Downloads

Published

2025-06-19

Issue

Section

Articles

How to Cite

Liu, Chunhan. 2025. “The Dynamics of Misinformation Spread on Social Media Networks”. Scientific Journal Of Humanities and Social Sciences 7 (7): 161-68. https://doi.org/10.54691/6gpycc30.