Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 607-618 |
| Number of pages | 12 |
| Journal | Discourse Processes |
| Volume | 55 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - 2018 |
Keywords
- BIDIMENSIONAL REGRESSION
- PERCEPTUAL SIMULATION
- MERE EXPOSURE
- ENCODES
- MAPS
- TIES
Cite this
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Extracting Social Networks from Language Statistics. / Hutchinson, Sterling; Louwerse, Max.
In: Discourse Processes, Vol. 55, No. 7, 2018, p. 607-618.Research output: Contribution to journal › Article › Scientific › peer-review
TY - JOUR
T1 - Extracting Social Networks from Language Statistics
AU - Hutchinson, Sterling
AU - Louwerse, Max
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - BIDIMENSIONAL REGRESSION
KW - PERCEPTUAL SIMULATION
KW - MERE EXPOSURE
KW - ENCODES
KW - MAPS
KW - TIES
U2 - 10.1080/0163853X.2017.1332446
DO - 10.1080/0163853X.2017.1332446
M3 - Article
VL - 55
SP - 607
EP - 618
JO - Discourse Processes
JF - Discourse Processes
SN - 0163-853X
IS - 7
ER -