Structure from interaction events
Bibliographic Data
| ID | 5260722 |
|---|---|
| Authors | W De Nooy (0000-0002-5644-5901, University of Amsterdam, corresponding author) |
| Year | 2015 |
| Volume | 2 |
| Issue | 2 |
| Pages | 2/2/2053951715603732 |
| Publication date | 2015-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Big Data & Society (JOURNAL) |
| Journal identifiers | ISSN: 2053-9517 • E-ISSN: 2053-9517 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/2053951715603732 |
| OpenAlex | W2189564562 |
| Language | EN |
| Citations received | 10 |
| References cited | 20 |
In this contribution to the colloquium, I argue why and how I lost interest in the overall structure of social networks even though Big Data techniques are increasingly simplifying the collection, organisation, and analysis of ever larger networks. The challenge that Big Data techniques pose to the social scientist, I think, is of a different nature. Big Data on social actors mainly record events, e.g. interactions between human beings that happen at a point in time. In contrast, social network analysts tend to think in terms of social relations that exist over a timespan. The challenge, then, is to rethink our conceptions and models of social relations and social structure. I conceptualize social structure and social relations as forces. I propose modelling these forces with regression models for longitudinal interaction data
Big data · Computational sociology · Data mining · Data science · Epistemology · Political science · Politics · Social media · Social relation · Social science · Social structure · Sociology · World Wide Web · Computer Science · Qualitative Comparative Analysis Research · Social and Cultural Dynamics · Social Capital and Networks
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| Unique citing works | 10 |
|---|---|
| Citations per year | 1,11 |
| Citation span | 2017 - 2023 (7) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 6 |