On the Relationship among User’s Reading Behavior, Algorithm Recommendation Mechanism and the Manufactured Filter Bubbles Effect

Abstract

This paper analyses the causal logic of algorithm recommendation and it employs the Pielou index to measure the distribution of news contents to provide empirical evidence to indicate whether the algorithm recommendation mechanism may produce filter bubbles. Moreover, this research takes Headlines Today as the research object to better understand the realization of tailored news and how their reading behaviour affect the algorithm recommendation mechanism. Meanwhile, the conclusion reinforces that users should enhance their information literacy in the era of artificial intelligence and big data, make rational use of algorithm recommendation mechanism, and pay close attention to the diversity of information sources to avoid information bias. This paper also helps the information flow platform to reflect on the shortcomings of the algorithm mechanism and optimise its strengths while avoiding those manufactured negative effects and proposes that in the optimisation of algorithm recommendation mechanism, the positive guidance to users should also be emphasised. Indicators such as content influence and mainstream media recommendation can be added to generate a multi-index recommendation.