Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2019 025

SONIC: SOcial Network with Influencers and Communities

Cathy Yi-Hsuan Chen
Wolfgang Karl Härdle
Yegor Klochkov

The integration of social media characteristics into an econometric framework
requires modeling a high dimensional dynamic network with dimensions of
parameter Θ typically much larger than the number of observations. To cope with
this problem, we introduce a new structural model — SONIC which assumes that
(1) a few influencers drive the network dynamics; (2) the community structure of
the network is characterized as the homogeneity of response to the specific
influencer, 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 the observed time interval is smaller than the size of the
network. Using a novel dataset retrieved from a leading social media platform–
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.

social media, network, community, opinion mining, natural language processing

JEL Classification:
C1, C22, C51, G41