Misinformation due to asymmetric information sharing
该研究构建了一个考虑不同类型信息具有不同衰减因子和共享网络不对称性的模型，解释了为何错误信息在社交媒体上长期占主导，并指出政策制定者应通过削弱回音室或增强枯燥信息的可分享性来缓解信息不对称。
On social media platforms, true and false information compete. Importantly, some messages travel much further than others, even if they concern the same topic. This fact is not reflected in models of social learning (or opinion formation) in networks. Our model fills this gap by allowing different types of information to have different decay factors and to be shared with different networks of people, incorporating asymmetries in sharing behaviors. More “shareable” information then dominates in the long run. This yields a substantial probability of misinformation, in contrast to the special case of symmetry covered by the literature. Asymptotic learning requires a perfect balance between two types of asymmetry: the product of decay factor and largest eigenvalue in the respective signal sharing networks must coincide. Approaching this balance reduces the speed of convergence and enables social learning in the shorter term. Our analysis thus suggests that policy makers, who do not know the true state, aim to mitigate asymmetries in signal sharing, e.g. by weakening echo chambers or by fostering the shareability of cumbersome, boring messages.