论文标题
通过随机轻推使社交网络上的意见去极化
Depolarization of opinions on social networks through random nudges
论文作者
论文摘要
在许多在线社交网络平台中,意见两极分化已得到经验。基于统计物理原理的传统意见动力学模型并未解释在线网络平台中两极分化和回声室的出现。最近引入的意见动力学模型结合了同质因素 - 代理与拥有与自己的观点相似的观点联系的趋势 - 捕获了两极分化和回声室效应。在这项工作中,我们为在线社区中的轻度推动代理人提供了一个非侵入性的框架,以形成随机连接。这表明这会导致观点的显着去极化并减少回声室效应。值得注意的是,即使是轻度的轻推也可以有效地避免极化,尽管大量轻推会导致另一种不良效果,即激进化。此外,我们获得了最佳的轻推因,以避免极化和激进结局的极端。
Polarization of opinions has been empirically noted in many online social network platforms. Traditional models of opinion dynamics, based on statistical physics principles, do not account for the emergence of polarization and echo chambers in online network platforms. A recently introduced opinion dynamics model that incorporates the homophily factor -- the tendency of agents to connect with those holding similar opinions as their own -- captures polarization and echo chamber effects. In this work, we provide a non-intrusive framework for mildly nudging agents in an online community to form random connections. This is shown to lead to significant depolarization of opinions and decrease the echo chamber effects. Remarkably, even a mild nudge is seen to be effective in avoiding polarization, though a large nudge leads to another undesirable effect, namely, radicalization. Further, we obtain the optimal nudge factor to avoid the extremes of polarization and radicalization outcomes.