Intermedia agenda-setting networks

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.

本研究探讨了噪声与拓扑结构对社交网络中观点多样性的影响。我们通过将成熟的观点动态模型拓展至随机分析框架中开展此项研究，在此框架下，智能体（agents）既会受到本地社交互动带来的同化性作用力影响，同时也会受阻碍群体达成共识的特质性因素的作用。我们针对该类特质性因素构建模型，以覆盖两种噪声场景：一种是噪声完全独立于同伴影响的外生场景，另一种是噪声源于智能体维持自身观点独特性意愿的内生场景。我们推导出适用于任意网络的观点多样性通用解析表达式，该表达式仅通过网络的谱特性来关联其拓扑结构。基于该表达式，我们发现随着社区与聚类结构的瓦解，观点多样性会随之降低。我们采用主流新闻媒体间的实证影响网络数据集对理论预测进行验证，结果显示：将我们提出的观点多样性测度纳入针对各类话题下媒体所表达情感倾向的线性模型后，模型的解释力得到了显著提升。