개인화 알고리즘으로 필터 버블이형성되는 과정에 대한 검
A Verification about the Formation Process of Filter Bubble with Personalization Algorithm
- 발행
- 2018
- 소속·발행
- 가천대학교
- 출처
- 국내 KCI
- DOI
- 10.9717/kmms.2018.21.3.369
- 원문등록
- 2018-03-30
- 원문
- 원문 보기 ↗
개념
키워드
Filter Bubble, Personalization Algorithm, LSTM, Recommender System, Political Polarization., Filter Bubble, Personalization Algorithm, LSTM, Recommender System, Political Polarization.
초록
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 formati