多状态选民模型中的过滤气泡效应
Filter Bubble effect in the multistate voter model
摘要：
社交媒体通过向用户推荐与他们过去偏好强烈相关的内容来影响在线活动。通过这种方式，它们将用户限制在过滤气泡内，从而严重限制了他们对新内容或替代内容的接触。我们通过考虑多州选民模型来研究这种类型的动态，其中，在给定的概率$λ$下，用户与暗示过去最常持有的观点的“个性化信息”进行交互。通过理论论证和数值模拟，我们表明存在一个非平凡的过渡区域（小$λ$）达成共识和区域（高于阈值$λ_c$），系统得到极化和集群的用户与不同的意见无限期地持续。对于大的系统大小$N$，阈值总是消失，这表明对于大量用户来说，共识变得不可能。这一发现开启了关于个性化推荐算法广泛使用的副作用的新问题。
英文摘要：
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.
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