检测和建模社交媒体上信息的真实渗透和相变
Detecting and modelling real percolation and phase transitions of information on social media
摘要：
人们普遍认为，社交媒体上的信息传播是一个渗透过程，与理论物理学中的相变相似。然而，这一假设的证据是有限的，因为相变还没有在任何社交媒体中直接观察到。在这里，通过对1亿微博和4000万Twitter用户的分析，我们发现了类似于广告的传播，并发现它比当前理论模型预测的更容易发生。较低的渗透阈值可以解释为网络结构和用户活跃度之间的协同进化中存在正反馈，使得更多的活跃用户获得更多的追随者。此外，这种共同进化导致了用户影响力的极端不平衡。我们的研究结果表明，信息在社交网络中传播的能力比预期的要高，这对许多信息传播问题都有影响。
英文摘要：
It is widely believed that information spread on social media is a percolation process, with parallels to phase transitions in theoretical physics. However, evidence for this hypothesis is limited, as phase transitions have not been directly observed in any social media. Here, through analysis of 100 million Weibo and 40 million Twitter users, we identify percolation-like spread, and find that it happens more readily than current theoretical models would predict. The lower percolation threshold can be explained by the existence of positive feedback in the coevolution between network structure and user activity level, such that more active users gain more followers. Moreover, this coevolution induces an extreme imbalance in users' influence. Our findings indicate that the ability of information to spread across social networks is higher than expected, with implications for many information spread problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
