Extracting Social Networks from Language Statistics
| Autor: | Max M. Louwerse, Sterling Hutchinson |
|---|---|
| Přispěvatelé: | Cognitive Science & AI |
| Rok vydání: | 2017 |
| Předmět: |
Linguistics and Language
Computer science Discourse analysis 050109 social psychology Semantics computer.software_genre BIDIMENSIONAL REGRESSION 050105 experimental psychology Language and Linguistics Interpersonal relationship TIES MAPS Statistics 0501 psychology and cognitive sciences ENCODES business.industry Latent semantic analysis Communication 05 social sciences Complex network Formal relationships MERE EXPOSURE PERCEPTUAL SIMULATION Artificial intelligence Computational linguistics business computer Natural language processing Network analysis |
| Zdroj: | Discourse Processes, 55(7), 607-618. ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD |
| ISSN: | 1532-6950 0163-853X |
| DOI: | 10.1080/0163853x.2017.1332446 |
| Popis: | Knowledge regarding social information is commonly believed to be derived from sources such as formal relationships and interviews and can be plotted as complex networks. We explored whether social networks can also be extracted through other means by using language statistics. In three computational studies we computed first-order and higher-order (latent semantic analysis) co-occurrences of story characters in three novels. These statistical linguistic frequencies entered in a multidimensional scaling analysis yielded a two-dimensional solution that correlated with the two-dimensional networks of characters generated by experts. An experimental study in which participants were asked to estimate social networks showed that human estimates are similar to computational estimates. These results demonstrated that language statistics based on texts can be used to generate social networks. |
| Databáze: | OpenAIRE |
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