Publication | Open Access
Close encounters of the conceptual kind: Disambiguating social structure from text
14
Citations
30
References
2015
Year
Social Data AnalysisEngineeringLexical SemanticsSemanticsCommunicationClose EncountersJournalismText MiningBig Data ModelApplied LinguisticsComputational Social ScienceSocial MediaEmpirical ProminenceNew MethodsDiscourse AnalysisLanguage StudiesSocial StructureConceptual AnalysisContent AnalysisConceptual KindInformation ManagementDiscourse StructureSocial ComputingKnowledge ManagementSocial InformaticsLinguisticsWord-sense DisambiguationBig Data
Despite its empirical prominence, there is very little extant organizational research on Big Data. However, there is reason to believe this is changing as organizational theory scholars are beginning to embrace new methods and data sources. In this essay, I present a view that suggests there are several latent opportunities, many of which have been simmering unattended for some time. This research approach is not without its challenges, as the ontological terrain of Big Data is untested and potentially disruptive. However, we are observing a renewal of approaches to text and content analysis. By opening up the toolkit of computational linguistics methods for text analysis, Big Data may bring about fresh synthesis and reshape classic debates around social structure.
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