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.

将社交媒体特征整合到计量经济学框架中需要对参数维度通常远大于观察次数的高维动态网络进行建模。为了解决这个问题，我们引入了 SONIC，这是一种新的高维网络模型，它假设（1）只有少数影响者驱动网络动态；(2) 网络的社区结构以对特定影响者的反应同质性为特征，暗示他们潜在的相似性。提出了一种基于贪心算法和LASSO正则化的估计过程。通过理论研究和模拟，我们表明即使样本量小于网络大小，也可以估计矩阵参数。使用从领先的社交媒体平台之一检索到的新数据集 — StockTwits 并通过自然语言处理量化他们的意见，我们对一组选定用户之间的意见网络动态进行建模，并进一步检测潜在社区。通过稀疏正则化，我们可以识别网络中的重要节点。