论文标题
噪音和拓扑对社交网络中意见动态的影响
The Impact Of Noise And Topology On Opinion Dynamics In Social Networks
论文作者
论文摘要
我们研究了噪声和拓扑对社交网络意见多样性的影响。我们这样做是通过将良好的意见动力学模型扩展到随机环境中，在这种环境中，代理人通过当地的社会互动而受到同化力量，以及特殊的因素，以阻止其人口达成共识。我们对后者进行建模，以说明两种情况，因为噪声完全是外在的，而噪声是同伴的影响以及它是内源性的情况，这是由于代理商渴望在他们的意见中保持独特性的愿望而产生的。我们为意见多样性提供了一般的分析表达，该表达可用于任何网络，仅通过其光谱属性来取决于网络的拓扑。使用此表达式，我们发现随着社区和集群的分解，意见多样性会降低。我们对描述主要新闻媒体之间的经验影响网络的数据测试我们的预测，发现将我们的度量纳入了这些来源对各种主题表达的情绪的线性模型中，从而在解释能力方面取得了显着改善。
We investigate the impact of noise and topology on opinion diversity in social networks. We do so by extending well-established models of opinion dynamics to a stochastic setting where agents are subject both to assimilative forces by their local social interactions, as well as to idiosyncratic factors preventing their population from reaching consensus. We model the latter to account for both scenarios where noise is entirely exogenous to peer influence and cases where it is instead endogenous, arising from the agents' desire to maintain some uniqueness in their opinions. We derive a general analytical expression for opinion diversity, which holds for any network and depends on the network's topology through its spectral properties alone. Using this expression, we find that opinion diversity decreases as communities and clusters are broken down. We test our predictions against data describing empirical influence networks between major news outlets and find that incorporating our measure in linear models for the sentiment expressed by such sources on a variety of topics yields a notable improvement in terms of explanatory power.
