References
[1]. Pang, G., Wang, X., Wang, L., Hao, F., Lin, Y., Wan, P., & Min, G. (2023). Efficient Deep Reinforcement Learning-Enabled Recommendation. IEEE Transactions on Network Science and Engineering, 10, 871-886. https://doi.org/10.1109/TNSE.2022.3224028.
[2]. Li, S., Karatzoglou, A., & Gentile, C. (2016). Collaborative Filtering.
[3]. Hashim, S., & Waden, J. (2023). Content-based filtering algorithm in social media. Wasit Journal of Computer and Mathematics Science. https://doi.org/10.31185/wjcm.112.
[4]. García, D., Kappas, A., Küster, D., & Schweitzer, F. (2016). The dynamics of emotions in online interaction. Royal Society Open Science, 3. https://doi.org/10.1098/rsos.160059.
[5]. Palmieri, E. (2024). Online bubbles and echo chambers as social systems. Kybernetes. https://doi.org/10.1108/k-09-2023-1742.
[6]. Hameleers, M. (2023). The (Un)Intended Consequences of Emphasizing the Threats of Mis- and Disinformation. Media and Communication. https://doi.org/10.17645/mac.v11i2.6301.
[7]. Sliwa, R. (2020). Disinformation campaigns in social media. https://doi.org/10.18419/OPUS-11202.
[8]. Bednar, J. (2021). Polarization, diversity, and democratic robustness. Proceedings of the National Academy of Sciences, 118. https://doi.org/10.1073/pnas.2113843118.
[9]. Vlasceanu, M., & Amodio, D. (2022). Propagation of societal gender inequality by internet search algorithms. Proceedings of the National Academy of Sciences of the United States of America, 119. https://doi.org/10.1073/pnas.2204529119.
[10]. Watson, H., & Nations, C. (2019). Addressing the Growing Need for Algorithmic Transparency. Commun. Assoc. Inf. Syst., 45, 26. https://doi.org/10.17705/1cais.04526.
[11]. Costanza-Chock, S., Raji, I., & Buolamwini, J. (2022). Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystem. Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. https://doi.org/10.1145/3531146.3533213.
Cite this article
Yang,K. (2025). Algorithmic Recommendation and Information Cocoons: Analysis of Information Security Issues on Social Media. Communications in Humanities Research,67,38-43.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Pang, G., Wang, X., Wang, L., Hao, F., Lin, Y., Wan, P., & Min, G. (2023). Efficient Deep Reinforcement Learning-Enabled Recommendation. IEEE Transactions on Network Science and Engineering, 10, 871-886. https://doi.org/10.1109/TNSE.2022.3224028.
[2]. Li, S., Karatzoglou, A., & Gentile, C. (2016). Collaborative Filtering.
[3]. Hashim, S., & Waden, J. (2023). Content-based filtering algorithm in social media. Wasit Journal of Computer and Mathematics Science. https://doi.org/10.31185/wjcm.112.
[4]. García, D., Kappas, A., Küster, D., & Schweitzer, F. (2016). The dynamics of emotions in online interaction. Royal Society Open Science, 3. https://doi.org/10.1098/rsos.160059.
[5]. Palmieri, E. (2024). Online bubbles and echo chambers as social systems. Kybernetes. https://doi.org/10.1108/k-09-2023-1742.
[6]. Hameleers, M. (2023). The (Un)Intended Consequences of Emphasizing the Threats of Mis- and Disinformation. Media and Communication. https://doi.org/10.17645/mac.v11i2.6301.
[7]. Sliwa, R. (2020). Disinformation campaigns in social media. https://doi.org/10.18419/OPUS-11202.
[8]. Bednar, J. (2021). Polarization, diversity, and democratic robustness. Proceedings of the National Academy of Sciences, 118. https://doi.org/10.1073/pnas.2113843118.
[9]. Vlasceanu, M., & Amodio, D. (2022). Propagation of societal gender inequality by internet search algorithms. Proceedings of the National Academy of Sciences of the United States of America, 119. https://doi.org/10.1073/pnas.2204529119.
[10]. Watson, H., & Nations, C. (2019). Addressing the Growing Need for Algorithmic Transparency. Commun. Assoc. Inf. Syst., 45, 26. https://doi.org/10.17705/1cais.04526.
[11]. Costanza-Chock, S., Raji, I., & Buolamwini, J. (2022). Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystem. Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. https://doi.org/10.1145/3531146.3533213.