Research on Cognitive Bias in Online Public Opinion
DOI:
https://doi.org/10.62051/ijcsit.v2n2.11Keywords:
Internet Public Opinion; Cognitive Bias; Big Data; User Influence; Algorithm FairnessAbstract
In the era of big data, there is a tremendous amount of information on the Internet, but people can only access part of it. Information obtained through only limited channels can easily cause cognitive bias and expand the influence of related online public opinions. This article explores the origins of bias, from the overwhelming influx of information to the personalized recommendation algorithms and homogeneity within social media groups. To combat these issues, the paper proposes solutions such as data preprocessing techniques, information dissemination methods, and measures to ensure algorithmic fairness. Beyond this, truly solving the problem requires interdisciplinary collaboration and multi-stakeholder engagement to effectively address these challenges, emphasizing efforts in public education, platform governance, and regulatory frameworks.
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Jin, D. X., Xia, Y. X., Bai, P. Y. et al. (2023) Research on risk assessment of emergency network public opinion for multi-dimensional risk scenarios. Information Research, (05), 17-24.
Wang, H., Luo, L., Qiao, J. (2023) Research on fuzzy comprehensive evaluation of risk of hot events on internet public opinion. Information Studies: Theory & Application, (11), 126-132.
Raza, S., Garg, M., Reji, D. J. et al. (2024) Nbias: A natural language processing framework for BIAS identification in text. Expert Systems with Applications, 237, 121542.
Pelrine, K., Danovitch, J., Rabbany, R. (2021) The surprising performance of simple baselines for misinformation detection. In: Proceedings of the Web Conference 2021. pp. 3432-3441.
Zhang, H.R. (2022) Research on fake information detection methods based on text feature and graph neural network. (Master's Degree, Central University of Finance and Economics)
Wu, J., Huang, X., He, C. C. et al. (2023) Propagation and evolution of public opinion in the outbreak of AIGC based on the theory of tipping point. Journal of Modern Information, 43, 145-161.
Wen, T., Chen, Y.W., abbas Syed, T. et al. (2024) ERIUE: Evidential reasoning-based influential users evaluation in social networks. Omega, 122, 102945.
Barry, L., Charpentier, A. (2023) Melting contestation: insurance fairness and machine learning. Ethics and Information Technology, 25(4), 49.
Zhang, W. Q., Li, Y. (2024) Fairness metrics of machine learning: review of status, challenges and future directions. Computer Science, (01), 266-272
Favier, M., Calders, T., Pinxteren, S. et al. (2023) How to be fair? a study of label and selection bias. Machine Learning, 112(12): 5081-5104.
Cheng, M., De-Arteaga, M., Mackey, L. et al. (2023) Social norm bias: residual harms of fairness-aware algorithms. Data Mining and Knowledge Discovery, 37(5), 1858-1884.
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Copyright (c) 2024 Jingqi Chen
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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