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dc.contributor.authorChen, C. Y-H.
dc.contributor.authorHärdle, W. K.
dc.contributor.authorKlochkov, Y.
dc.date.accessioned2020-01-10T14:52:17Z
dc.date.available2020-01-10T14:52:17Z
dc.date.issued2019-12-17
dc.identifier.otherCWPE1998
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/300746
dc.description.abstractIntegration 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.
dc.relation.ispartofseriesCambridge Working Papers in Economics
dc.rightsAll Rights Reserved
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserved/
dc.subjectSocial Media
dc.subjectNetwork
dc.subjectCommunity
dc.subjectOpinion Mining
dc.subjectNatural Language Processing
dc.titleInfluencers and Communities in Social Networks
dc.typeWorking Paper
dc.identifier.doi10.17863/CAM.47819


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