Updating the Gatekeeper in the New Media Age: The Algorithm

Research Article
Open access

Updating the Gatekeeper in the New Media Age: The Algorithm

Jiayi Wang 1*
  • 1 Faculty of Humanities and Social Sciences, Beijing Normal University - Hong Kong Baptist University United International College, Zhuhai, 519000, China    
  • *corresponding author r130031240@mail.uic.edu.cn
LNEP Vol.4
ISSN (Print): 2753-7056
ISSN (Online): 2753-7048
ISBN (Print): 978-1-915371-33-1
ISBN (Online): 978-1-915371-34-8

Abstract

In the era of new media, an algorithmic recommendation mechanism is widely used by social media platforms as a new type of gatekeeper and has greatly affected people's entertainment methods and habits. Algorithms collect and analyze user data and then recommend similar content to users based on relevant tags and keywords. Although this provides users with a personalized experience, this personalized service unconsciously forms an information cocoon, which can easily limit people's cognition. It is the purpose of this article to let the audience understand the impact of the algorithmic recommendation mechanism and to reduce the harm to the audience's cognition under the algorithmic recommendation mechanism. Through reading and citing various literature studies, this paper clarifies the theoretical positioning of algorithmic recommendation mechanisms in gatekeeper theory and enumerates the positive impact of algorithmic recommendation mechanisms on individuals, social media platforms and society along with the development of social media platforms, as well as the negative impact on individuals and society. Finally, through the negative impact of the algorithmic recommendation mechanism, this paper discusses how to break the information cocoon room and reduce the negative impact of the algorithmic recommendation mechanism on the audience's cognition from three dimensions: individual audiences, mainstream media, and social media platforms.

Keywords:

Algorithmic, social media, gatekeeper, information cocoon, cognition

Wang,J. (2023). Updating the Gatekeeper in the New Media Age: The Algorithm. Lecture Notes in Education Psychology and Public Media,4,293-297.
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References

[1]. Shoemaker, P. J., & Han, G. (2020). The Supra-Gatekeepers: Gatekeeping in Age of Social Media. Journal of Communication and Society, 54, 223-256.

[2]. Na, X. T. (2022). The dilemma of TikTok users under intelligent algorithm and exploration of breakthrough paths. Journal of News Research, 13(10), 1-3.

[3]. Yao, M. (2020). Research on the Subjectivity of Journalists in the Algorithmic Recommendation Mode. China Journal of Radio and Television, 349(4), 51–54.

[4]. Zhang, X. X. (2017). The Idea, Significance and Ethical Risk of Personalized Recommendation of Algorithmic News. Media, 11, 82–84.

[5]. Li K. H., Yang Q., & Wu G. R. (2022). Technological concerns and good governance of mainstream ideology in the field of algorithm recommendation. Science and Technology Communication, (03), 103-105.

[6]. Fuchs, C. (2014). Social Media: A Critical Introduction. London: SAGE.

[7]. Etter, M., &Albu, O. B. (2021). Activists in the dark: Social media algorithms and collective action in two social movement organizations. Organization, 28(1), 68-91.

[8]. Coretti, L. and Pica, D. (2018) ‘Facebook’s Communication Protocols, Algorithmic Filters, and Protest. A Critical Socio-technical Perspective’, in M. Mortensen, C. Neumayer and T. Poell (eds), Social Media, Materialities, and Protest: Critical Reflections. London, UK: Routledge, pp. 81–100.

[9]. Schroeder, J. E. (2021). Reinscribing gender: social media, algorithms, bias. Journal of Marketing Management, 37, 3-4, 376-378. https://doi.org/10.1080/0267257X.2020.1832378.

[10]. Mishra, A., Mishra, H., & Rathee, S. (2019). Examining the presence of gender bias in customer reviews using word embedding. (Working paper). David Eccles School of Business, University of Utah. Available at SSRN https://ssrn.com/abstract=3327404.

[11]. Zheng, M. N, & Li, B. (2022). Information cocooning in social networks in the context of public opinion governance and the way to break the cocoon. Journal of Southwest University for Nationalities (Humanities and Social Sciences Edition), 43(04),140-144.

[12]. Fang, W. L. (2019). Research on media Ethics anomie under algorithmic recommendation model. Shandong Industrial Technology, (7), 138.

Cite this article

Wang,J. (2023). Updating the Gatekeeper in the New Media Age: The Algorithm. Lecture Notes in Education Psychology and Public Media,4,293-297.

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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About volume

Volume title: Proceedings of the International Conference on Interdisciplinary Humanities and Communication Studies (ICIHCS 2022), Part 3

ISBN：978-1-915371-33-1(Print) / 978-1-915371-34-8(Online)
Editor：Muhammad Idrees, Matilde Lafuente-Lechuga
Conference website: https://www.icihcs.org/
Conference date: 18 December 2022
Series: Lecture Notes in Education Psychology and Public Media
Volume number: Vol.4
ISSN：2753-7048(Print) / 2753-7056(Online)

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References

[1]. Shoemaker, P. J., & Han, G. (2020). The Supra-Gatekeepers: Gatekeeping in Age of Social Media. Journal of Communication and Society, 54, 223-256.

[2]. Na, X. T. (2022). The dilemma of TikTok users under intelligent algorithm and exploration of breakthrough paths. Journal of News Research, 13(10), 1-3.

[3]. Yao, M. (2020). Research on the Subjectivity of Journalists in the Algorithmic Recommendation Mode. China Journal of Radio and Television, 349(4), 51–54.

[4]. Zhang, X. X. (2017). The Idea, Significance and Ethical Risk of Personalized Recommendation of Algorithmic News. Media, 11, 82–84.

[5]. Li K. H., Yang Q., & Wu G. R. (2022). Technological concerns and good governance of mainstream ideology in the field of algorithm recommendation. Science and Technology Communication, (03), 103-105.

[6]. Fuchs, C. (2014). Social Media: A Critical Introduction. London: SAGE.

[7]. Etter, M., &Albu, O. B. (2021). Activists in the dark: Social media algorithms and collective action in two social movement organizations. Organization, 28(1), 68-91.

[8]. Coretti, L. and Pica, D. (2018) ‘Facebook’s Communication Protocols, Algorithmic Filters, and Protest. A Critical Socio-technical Perspective’, in M. Mortensen, C. Neumayer and T. Poell (eds), Social Media, Materialities, and Protest: Critical Reflections. London, UK: Routledge, pp. 81–100.

[9]. Schroeder, J. E. (2021). Reinscribing gender: social media, algorithms, bias. Journal of Marketing Management, 37, 3-4, 376-378. https://doi.org/10.1080/0267257X.2020.1832378.

[10]. Mishra, A., Mishra, H., & Rathee, S. (2019). Examining the presence of gender bias in customer reviews using word embedding. (Working paper). David Eccles School of Business, University of Utah. Available at SSRN https://ssrn.com/abstract=3327404.

[11]. Zheng, M. N, & Li, B. (2022). Information cocooning in social networks in the context of public opinion governance and the way to break the cocoon. Journal of Southwest University for Nationalities (Humanities and Social Sciences Edition), 43(04),140-144.

[12]. Fang, W. L. (2019). Research on media Ethics anomie under algorithmic recommendation model. Shandong Industrial Technology, (7), 138.

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