Close encounters of the conceptual kind: Disambiguating social structure from text
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 sev...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2015-11-01
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| Series: | Big Data & Society |
| Online Access: | https://doi.org/10.1177/2053951715608655 |
| _version_ | 1818899574074376192 |
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| author | Timothy Hannigan |
| author_facet | Timothy Hannigan |
| author_sort | Timothy Hannigan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T19:50:07Z |
| format | Article |
| id | doaj.art-936c0a4e23764286bdfc5bc388fb6f4f |
| institution | Directory Open Access Journal |
| issn | 2053-9517 |
| language | English |
| last_indexed | 2024-12-19T19:50:07Z |
| publishDate | 2015-11-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Big Data & Society |
| spelling | doaj.art-936c0a4e23764286bdfc5bc388fb6f4f2022-12-21T20:08:01ZengSAGE PublishingBig Data & Society2053-95172015-11-01210.1177/205395171560865510.1177_2053951715608655Close encounters of the conceptual kind: Disambiguating social structure from textTimothy HanniganDespite 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.https://doi.org/10.1177/2053951715608655 |
| spellingShingle | Timothy Hannigan Close encounters of the conceptual kind: Disambiguating social structure from text Big Data & Society |
| title | Close encounters of the conceptual kind: Disambiguating social structure from text |
| title_full | Close encounters of the conceptual kind: Disambiguating social structure from text |
| title_fullStr | Close encounters of the conceptual kind: Disambiguating social structure from text |
| title_full_unstemmed | Close encounters of the conceptual kind: Disambiguating social structure from text |
| title_short | Close encounters of the conceptual kind: Disambiguating social structure from text |
| title_sort | close encounters of the conceptual kind disambiguating social structure from text |
| url | https://doi.org/10.1177/2053951715608655 |
| work_keys_str_mv | AT timothyhannigan closeencountersoftheconceptualkinddisambiguatingsocialstructurefromtext |