October 25, 2019
Joint CORE/ISBA seminar
SONIC: Social Network with Influencers and Communities
Wolfgang Härdle, Humboldt-Universität zu Berlin, Germany
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 which supposes that the network is driven by inﬂuencers. We additionally assume the community structure of the network, such that the users from the same community depend on the same inﬂuencers. An estimation procedure is proposed based on a greedy algorithm and LASSO. 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 of 1069K messages from 30K users posted on the microblogging platform StockTwits during a 4-year period (01.2014-12.2018) and quantifying their opinions via natural language processing, we model their dynamic opinions network and further separate the network into communities. With a sparsity regularization, we are able to identify important nodes in the network.