Discovering influencers in opinion formation over social graphs

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buir.contributor.authorCemri, Mert
dc.citation.epage207en_US
dc.citation.spage188
dc.citation.volumeNumber4
dc.contributor.authorShumovskaia, V.
dc.contributor.authorKayaalp, M.
dc.contributor.authorCemri, Mert
dc.contributor.authorSayed, A. H.
dc.date.accessioned2024-03-18T07:58:32Z
dc.date.available2024-03-18T07:58:32Z
dc.date.issued2023-03-23
dc.departmentDepartment of Electrical and Electronics Engineering
dc.description.abstractThe adaptive social learning paradigm helps model how networked agents are able to form opinions on a state of nature and track its drifts in a changing environment. In this framework, the agents repeatedly update their beliefs based on private observations and exchange the beliefs with their neighbors. In this work, it is shown how the sequence of publicly exchanged beliefs over time allows users to discover rich information about the underlying network topology and about the flow of information over the graph. In particular, it is shown that it is possible (i) to identify the influence of each individual agent to the objective of truth learning, (ii) to discover how well-informed each agent is, (iii) to quantify the pairwise influences between agents, and (iv) to learn the underlying network topology. The algorithm derived herein is also able to work under non-stationary environments where either the true state of nature or the graph topology are allowed to drift over time. We apply the proposed algorithm to different subnetworks of Twitter users, and identify the most influential and central agents by using their public tweets (posts).
dc.identifier.doi10.1109/OJSP.2023.3261132
dc.identifier.eissn2644-1322
dc.identifier.urihttps://hdl.handle.net/11693/114862
dc.language.isoEnglish
dc.publisherInstitute of Electrical and Electronics Engineers
dc.relation.isversionofhttps://dx.doi.org/10.1109/OJSP.2023.3261132
dc.source.titleIEEE Open Journal of Signal Processing
dc.subjectSocial learning
dc.subjectSocial influence
dc.subjectExplainability
dc.subjectInverse modeling
dc.subjectOnline learning
dc.subjectGraph learning
dc.subjectTwitter
dc.titleDiscovering influencers in opinion formation over social graphs
dc.typeArticle

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