Lecture Notes in Education Psychology and Public Media

- The Open Access Proceedings Series for Conferences


Lecture Notes in Education Psychology and Public Media

Vol. 3, 01 March 2023


Open Access | Article

Investigation of Users’ Experience of Social Media’s Personalized Recommendation — The Case of Xiaohongshu

Shufan Yu 1
1 College of Journalism and communication, Chinese Culture University, Taipei, Taiwan, 11114, China

* Author to whom correspondence should be addressed.

Lecture Notes in Education Psychology and Public Media, Vol. 3, 49-60
Published 01 March 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Shufan Yu. Investigation of Users’ Experience of Social Media’s Personalized Recommendation — The Case of Xiaohongshu. LNEP (2023) Vol. 3: 49-60. DOI: 10.54254/2753-7048/3/2022456.

Abstract

This research was conducted on Chinese participants between 18 and 25. A self-administered questionnaire was developed and distributed to 475 Xiaohongshu users. This research investigated how are Chinese young adults affected by what the Personalized Recommendations of Xiaohongshu show them and how Xiaohongshu is engineering its platform so that its users “trust” it. This research also explored whether Xiaohongshu’s users want to opt out of their AI bubble or do they prefer the way the platform tailors the world to their interests. The results suggested that participants tended to accept the homogeneity content pushed by Xiaohongshu and ignore the information missing because of the personalized recommendation. This research also indicated that users could not wholly jump out of the filter bubbles, though some of them are willing to do it. This research was an addition to the literature on the tension in social media between product promotion and privacy.

Keywords

Users’ experience, Xiaohongshu, Personalized Recommendation, Homogeneity content.

References

1. CGTNGGlobalBusiness(2022). Issue 290: Xiaohongshu repeatedly touches the red line. The platform operation must be in legal compliance. Retrieved from https://m.weibo.cn/6179517662/4716951937749435

2. Weilun Soon(2022, March). Xiaohongshu is China’s Instagram on steroids, blending influencers and shopping. Here's how it works. The Insider. Retrieved from https://www.businessinsider.com/china-instagram-killer-xiaohongshu-explained-2022-3

3. Nancy Cao(2020). Being a KOL of Xiaohongshu is not just about luck. Retrieved from https://www.zhihu.com/market/paid_column/1257343431132434432/section/1271124811431985152?is_share_data=true

4. Pariser, E. (2011). The filter bubble: How the new personalized web is changing what we read and how we think. Penguin.

5. Xiaohongshu(2022). Xiaohongshu’s Personalized Recommendation Algorithm Description.

6. Weber, D., Shirazi, A. S., & Henze, N. (2015, August). Towards smart notifications using research in the large. In Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct (pp. 1117-1122).

7. Gavilan, D., Fernández-Lores, S., & Martinez-Navarro, G. (2020). Vividness of news push notifications and users’ response. Technological Forecasting and Social Change, 161, 120281.

8. Akar, E., & Mardikyan, S. (2014). Analyzing factors affecting users' behavior intention to use social media: Twitter case. International Journal of Business and Social Science, 5(11).

9. Eslami, M., Rickman, A., Vaccaro, K., Aleyasen, A., Vuong, A., Karahalios, K., ... & Sandvig, C. (2015, April). " I always assumed that I wasn't really that close to [her]" Reasoning about Invisible Algorithms in News Feeds. In Proceedings of the 33rd annual ACM conference on human factors in computing systems (pp. 153-162).

10. Alex Hern. (2017, May) How social media filter bubbles and algorithms influence the election. The Guardian. Retrieved from https://www.theguardian.com/technology/2017/may/22/social-media-election-facebook-filter-bubbles

11. Pariser, E. (2011) The Filter Bubble: What the Internet is Hiding from You. Penguin Press, New York, NY

12. Röchert, D., Weitzel, M., & Ross, B. (2020, July). The homogeneity of right-wing populist and radical content in YouTube recommendations. In International Conference on Social Media and Society (pp. 245-254).

13. Koenig, A. (2020). The algorithms know me and i know them: using student journals to uncover algorithmic literacy awareness. Computers and Composition, 58, 102611.

14. Yu, C., Zhang, Z., Lin, C., & Wu, Y. J. (2020). Can data-driven precision marketing promote user AD clicks? Evidence from advertising in WeChat moments. Industrial Marketing Management, 90, 481-492.

15. Ghosh, D., & Scott, B. (2018). Digital deceit: the technologies behind precision propaganda on the internet.

16. Sam Jossen (2017,May)The World’s Most Valuable Resource is No Longer Oil, But Data. The Economist. Retrieved from https://www.rga.com/ja/futurevision/pov/the-worlds-most-valuable-resource-is-no-longer-oil-but-data-4792050

17. Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12), 61-70.

18. DeVito, M. A., Gergle, D., & Birnholtz, J. (2017, May). “ Algorithms ruin everything” # RIPTwitter, Folk Theories, and Resistance to Algorithmic Change in Social Media. In Proceedings of the 2017 CHI conference on human factors in computing systems (pp. 3163-3174).

19. Barki, H., & Hartwick, J. (1994). Measuring user participation, user involvement, and user attitude. MIS quarterly, 59-82.

20. Lalmas, M., O'Brien, H., & Yom-Tov, E. (2014). Measuring user engagement. Synthesis lectures on information concepts, retrieval, and services, 6(4), 1-132.

21. Eslami, M., Rickman, A., Vaccaro, K., Aleyasen, A., Vuong, A., Karahalios, K., ... & Sandvig, C. (2015, April). " I always assumed that I wasn't really that close to [her]" Reasoning about Invisible Algorithms in News Feeds. In Proceedings of the 33rd annual ACM conference on human factors in computing systems (pp. 153-162).

22. Joe Dawson.( 2021). Seven first-party data capturing opportunities your business is missing out on. Retrieved from https://www.searchenginewatch.com/2021/04/29/seven-first-party-data-capturing-opportunities-your-business-is-missing-out-on/

23. Bozdag, E. (2013). Bias in algorithmic filtering and personalization. Ethics and information technology, 15(3), 209-227.

24. Wei, Ru-Qing & Tang, Fang-Cheng. (2016). The social influence mechanism of user-generated content on online shopping-an empirical analysis based on social e-commerce. East China Economic Management (04), 124-131.

25. Xu, Hui-Li. (2017). Analysis of mobile community e-commerce business model and strategy--Taking "Xiaohongshu" as an example. Market Week (Theory Research) (09), 67-68.

26. Zhu, Ying-Ying. (2018). Successful experience and inspiration of closed-loop operation of Little Red Book cross-border e-commerce platform. Foreign Economic and Trade Practice (08), 93-96.

27. Zhu J. (2021). "Netflix economy" and "emotional labor"-a perspective on understanding "Xiaohongshu". Literary Theory and Criticism (01), 77-87. doi:10.16532/j.cnki.1002-9583.2021.01.006.

28. Evans, J. R., & Mathur, A. (2005). The value of online surveys. Internet research.

29. Harris, C., & Laibson, D. (2004). Instantaneous gratification. mimeo.

30. Bakshy, E., Messing, S., & Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on Facebook. Science, 348(6239), 1130-1132.

31. Hodkinson, P. (2017). Bedrooms and beyond: Youth, identity and privacy on social network sites. New Media & Society, 19(2), 272-288. Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, communication & society, 15(5), 662-679.

32. Chen, Z. T., & Cheung, M. (2018). Privacy perception and protection on Chinese social media: A case study of WeChat. Ethics and Information Technology, 20(4), 279-289.

Data Availability

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

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Authors who publish this series agree to the following terms:

1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.

2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.

3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open Access Instruction).

Volume Title
Proceedings of the 3rd International Conference on Educational Innovation and Philosophical Inquiries (ICEIPI 2022), Part II
ISBN (Print)
978-1-915371-09-6
ISBN (Online)
978-1-915371-10-2
Published Date
01 March 2023
Series
Lecture Notes in Education Psychology and Public Media
ISSN (Print)
2753-7048
ISSN (Online)
2753-7056
DOI
10.54254/2753-7048/3/2022456
Copyright
01 March 2023
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated