Electronic Theses and Dissertations

Identifier

819

Date

2013

Date of Award

4-18-2013

Document Type

Thesis

Degree Name

Master of Science

Major

Psychology

Concentration

General Psychology

Committee Chair

Max Louwerse

Committee Member

Evan Risko

Committee Member

George Relyea

Abstract

Knowledge regarding social information is commonly thought to be derived from sources such as interviews and formal relationships. Consequently, social networks can be generated from this information. Recent work has demonstrated that language statistics can explain findings often thought to primarily be explained by external factors. Three studies explored whether language implicitly comprises information that allows for extracting social networks, by testing the hypothesis that individuals who are socially related together are linguistically discussed together, as well as the hypothesis that individuals who are socially related more are linguistically discussed more. Three computational studies were conducted testing the extent to which social networks could be extracted from fiction novels. Semantic relationships revealed that MDS solutions correlated with the actual social network of characters. A human study in which participants estimated social relationships of characters matched the results obtained computationally. The results demonstrated that linguistic information encodes social relationship information.

Comments

Data is provided by the student.

Library Comment

dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.

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