ABSTRACT
Over-personalized recommendation algorithms have led to various invisible filtering bubbles in the internet society, which constantly affect the internet environment and social ecology, causing some social impacts worth attention and some social problems that must be addressed. This paper starts from the generation, concept definition, and implications of filtering bubbles, fully recognizing the meaning of "filtering bubbles" and their impact on society, and puts forward some suggestions for media platforms to "pop bubbles". The paper mainly provides suggestions for media platforms to "pop bubbles" from three aspects: reducing or eliminating the use of recommendation algorithms, anti-personalized recommendations, and diversified packaging and combination of personalized recommendation content, and also put forward some thoughts on the future development direction of the field.
- Pariser E. The filter bubble:what the Internet is hiding from you[M].New York: The Penguin Press,2011: 10.Google Scholar
- Glick, J., Rise of the platishers. vox.com, 2014.Google Scholar
- Aurora Big Data. Aurora Big Data News App Report. 2017/3/30; Available at:http://www.199it.com/archives/577612.html.Google Scholar
- Sina Technology. QuestMobile: 2017 China Mobile Internet Annual Report. March 11, 2018; Available at: http:/ /tech. sina. com. cn//2018-01-17/doc- ifyqqieu7074004. html.Google Scholar
- Zhang Z. A. and Tang M., On the impact of algorithmic recommendation on mainstream ideological communication. Social Science Front, 2018(10): 174- 182.Google Scholar
- Sunstein C R. Infotopia: How many minds produce knowledge[M]. Oxford University Press, 2006.Google ScholarCross Ref
- Holone, H., The filter bubble and its effect on online personal health information. croatian medical journal, 2016. 57(3): p. 298.Google Scholar
- Capurro, G., , Measles, moral regulation and the social construction of risk: media narratives of "anti-vaxxers" and the 2015 Disneyland outbreak. Canadian Journal of Sociology/Cahiers canadiens de sociologie, 2018. 43(1):p. 25-47.Google Scholar
- Wang Bin and Li Wanzhen, How to burst the "filter bubble" cognitive narrowing in algorithmic push news and its circumvention. News & Writing, 2018(9): p. 20-26.Google Scholar
- Faridani, S., Opinion space: a scalable tool for browsing online comments. in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2010.Google ScholarDigital Library
- Kriplean, T., Supporting reflective public thought with considerit. in Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work. 2012.Google ScholarDigital Library
- Xing, X., Exposing inconsistent web search results with bobble. in Passive and Active Measurement: 15th International Conference, PAM 2014, Los Angeles, CA, USA, March 10-11, 2014, Proceedings 15. 2014. springer.Google ScholarDigital Library
- Ciobanu, M., NZZ is developing an app that gives readers personalised news without creating a filter bubble.Retrieved. jan.19, 2018Google Scholar
- Peng Yanlin, A review of research related to the "filter bubble" phenomenon in personalized recommendations. Technology Entrepreneurship Monthly, 2019. 32(04): p. 135-139.Google Scholar
Index Terms
- Avoiding the impact of “Filter Bubbles” – Take “Internet Doctor” as example
Recommendations
Filter bubbles created by collaborative filtering algorithms themselves, fact or fiction? An experimental comparison
WI-IAT '21: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent TechnologyBy recommending only, or mostly, items close to our supposedly known tastes, a recommender system could lead to the narrowing of the diversity/novelty of the recommended list, leading to the creation of so-called filter bubbles. Authors however ...
Exploring the filter bubble: the effect of using recommender systems on content diversity
WWW '14: Proceedings of the 23rd international conference on World wide webEli Pariser coined the term 'filter bubble' to describe the potential for online personalization to effectively isolate people from a diversity of viewpoints or content. Online recommender systems - built on algorithms that attempt to predict which ...
CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System
While personalization increases the utility of recommender systems, it also brings the issue of filter bubbles. e.g., if the system keeps exposing and recommending the items that the user is interested in, it may also make the user feel bored and less ...
Comments