Generalizing from social media data: a formaltheory approach
| dc.contributor.author | Davis, Jenny L | |
| dc.contributor.author | Love, Tony | |
| dc.date.accessioned | 2022-03-28T03:22:43Z | |
| dc.date.issued | 2019 | |
| dc.date.updated | 2020-12-20T07:37:13Z | |
| dc.description.abstract | Researchers increasingly draw on social media data to answer big questions about social patterns and dynamics. However, as with any data source, social media data present both opportunities and significant challenges. One major critique of social media data is that the data are not generalizable outside of the platforms from which the data originate. Problems of generalizability stem from non-universal participation rates on various platforms, demographically biased samples, as well as limited access to data based on infrastructural constraints and/or user privacy practices. We suggest that instead of empirical generalizability, social media data are theoretically generalizable in the formal theory tradition. Through a case example in which we use YouTube comments to test and extend a key tenet of identity theory, we show how social media data can instantiate theoretical variables and thus generalize to theoretical propositions. Mediated through formal theory, social media data maintain the capacity to address broad social questions while upholding methodological integrity | en_AU |
| dc.format.mimetype | application/pdf | en_AU |
| dc.identifier.issn | 1369-118X | en_AU |
| dc.identifier.uri | http://hdl.handle.net/1885/262722 | |
| dc.language.iso | en_AU | en_AU |
| dc.publisher | Brunner - Routledge (US) | en_AU |
| dc.rights | © 2019 | en_AU |
| dc.source | Information Communication and Society | en_AU |
| dc.subject | Generalizability | en_AU |
| dc.subject | social media | en_AU |
| dc.subject | formal theory | en_AU |
| dc.subject | research methods; | en_AU |
| dc.subject | big data | en_AU |
| dc.title | Generalizing from social media data: a formaltheory approach | en_AU |
| dc.type | Journal article | en_AU |
| local.bibliographicCitation.issue | 5 | en_AU |
| local.bibliographicCitation.lastpage | 647 | en_AU |
| local.bibliographicCitation.startpage | 637 | en_AU |
| local.contributor.affiliation | Davis, Jennifer (Jenny), College of Arts and Social Sciences, ANU | en_AU |
| local.contributor.affiliation | Love, Tony, College of Arts and Social Sciences, ANU | en_AU |
| local.contributor.authoruid | Davis, Jennifer (Jenny), u1027756 | en_AU |
| local.contributor.authoruid | Love, Tony, u1072014 | en_AU |
| local.description.embargo | 2099-12-31 | |
| local.description.notes | Imported from ARIES | en_AU |
| local.identifier.absfor | 160807 - Sociological Methodology and Research Methods | en_AU |
| local.identifier.absfor | 160808 - Sociology and Social Studies of Science and Technology | en_AU |
| local.identifier.absseo | 970108 - Expanding Knowledge in the Information and Computing Sciences | en_AU |
| local.identifier.ariespublication | u5423761xPUB28 | en_AU |
| local.identifier.citationvolume | 22 | en_AU |
| local.identifier.doi | 10.1080/1369118X.2018.1555610 | en_AU |
| local.identifier.scopusID | 2-s2.0-85058220139 | |
| local.publisher.url | https://www.tandfonline.com/ | en_AU |
| local.type.status | Published Version | en_AU |
Downloads
Original bundle
1 - 1 of 1
Loading...
- Name:
- 01_Davis_Generalizing_from_social_media_2019.pdf
- Size:
- 1.17 MB
- Format:
- Adobe Portable Document Format