论文标题
多态选民模型中的滤泡泡沫效应
Filter Bubble effect in the multistate voter model
论文作者
论文摘要
社交媒体通过推荐用户与过去喜欢的内容密切相关的内容来影响在线活动。通过这种方式，它们会在过滤气泡中限制用户，从而极大地限制了他们对新内容或替代内容的影响。我们通过考虑多台选民模型来研究这种动态，其中用户使用给定的概率$λ$与“个性化信息”进行互动，这表明过去最常持有的意见。通过理论论点和数值模拟，我们表明了一个区域（对于小$λ$）之间的非平凡过渡的存在，在该区域达成共识，并且在该区域（高于阈值$λ_C$）的区域（超过阈值$λ_C$）中，该系统具有极化和具有不同意见的用户的群体持续无限。对于大型系统尺寸$ n $，阈值总是消失，表明大量用户的共识是不可能的。这一发现为广泛使用个性化推荐算法的副作用打开了新问题。
Social media influence online activity by recommending to users content strongly correlated with what they have preferred in the past. In this way they constrain users within filter bubbles that strongly limit their exposure to new or alternative content. We investigate this type of dynamics by considering a multistate voter model where, with a given probability $λ$, a user interacts with a "personalized information" suggesting the opinion most frequently held in the past. By means of theoretical arguments and numerical simulations, we show the existence of a nontrivial transition between a region (for small $λ$) where consensus is reached and a region (above a threshold $λ_c$) where the system gets polarized and clusters of users with different opinions persist indefinitely. The threshold always vanishes for large system size $N$, showing that consensus becomes impossible for a large number of users. This finding opens new questions about the side effects of the widespread use of personalized recommendation algorithms.
