Abstrakt
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. (c) 2021 The Author(s).
Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).