The integration of social media characteristics into an econometric framework requires modeling a high dimensional dynamic network with dimensions of parameter typically much larger than the number of observations. To cope with this problem, we introduce SONIC, a new high-dimensional network model that assumes that (1) only few influencers drive the network dynamics; (2) the community structure of the network is characterized by homogeneity of response to specific influencers, implying their underlying similarity. An estimation procedure is proposed based on a greedy algorithm and LASSO regularization. Through theoretical study and simulations, we show that the matrix parameter can be estimated even when sample size is smaller than the size of the network. Using a novel dataset retrieved from one of leading social media platforms - StockTwits and quantifying their opinions via natural language processing, we model the opinions network dynamics among a select group of users and further detect the latent communities. With a sparsity regularization, we can identify important nodes in the network.

翻译：将社交媒体特点纳入计量经济学框架,需要建模一个高维动态网络,其参数范围通常比观测数量大得多。为了解决这一问题,我们引入了SONIC,这是一个新的高维网络模型,假设:(1) 影响者很少驱动网络动态;(2) 网络社区结构的特点是对特定影响者的反应具有同质性,意味着其内在相似性。根据贪婪的算法和LASOS正规化提出了估算程序。通过理论研究和模拟,我们显示即使在样本规模小于网络规模的情况下,矩阵参数也可以估算。我们利用从一个主要社会媒体平台中检索的新数据集-StockTwiters和通过自然语言处理量化其观点,我们将特定用户群体的意见网络动态建模,并进一步检测潜在社区。有了宽阔度规范,我们就可以发现网络中的重要节点。