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http://dx.doi.org/10.9717/kmms.2018.21.3.369

A Verification about the Formation Process of Filter Bubble with Personalization Algorithm  

Jun, Junyong (Dept. of Computer Engineering, Gachon University)
Hwang, Soyoun (Dept. of IT Convergence Engineering, Gachon University)
Yoon, Youngmi (Dept. of Computer Engineering, Gachon University)
Publication Information
Journal of Korea Multimedia Society / v.21, no.3, 2018 , pp. 369-381 More about this Journal
Abstract
Nowadays a personalization algorithm is gaining huge attention. It gives users selective information which is helpful and interesting in a deluge of information based on their past behavior on the internet. However there is also a fatal side effect that the user can only get restricted information on restricted topics selected by the algorithm. Basically, the personalization algorithm makes users have a narrower perspective and even stronger bias because users have less chances to get views of opponent. Eli Pariser called this problem the 'filter bubble' in his book. It is important to understand exactly what a filter bubble is to solve the problem. Therefore, this paper shows how much Google's personalized search algorithm influences search result through an experiment with deep neural networks acting like users. At the beginning of the experiment, two Google accounts are newly created, not to be influenced by the Google's personalized search algorithm. Then the two pure accounts get politically biased by two methods. We periodically calculate the numerical score depending on the character of links and it shows how biased the account is. In conclusion, this paper shows the formation process of filter bubble by a personalization algorithm through the experiment.
Keywords
Filter Bubble; Personalization Algorithm; LSTM; Recommender System; Political Polarization;
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