Celebrity profiling through linguistic analysis of digital social networks

Computational Social Networks

Table 22 Model of the characteristic of the person Birth year

Feature groups	Variable	1950		1960		1970		1980
Feature groups	Predictor	Estimator	P-value	Estimator	P-value	Estimator	P-value	Estimator	P-value
Lexical	(Intercept)	1.05	0.099	3.22	<0.001	5.61	<0.001	6.94	<0.001
	V1	− 0.01	0.561	− 0.02	0.043	− 0.04	<0.001	− 0.13	<0.001
	V3	− 2.85	0.166	− 1.34	0.411	− 2.69	0.094	− 2.26	0.153
	V4	− 0.09	0.517	− 0.64	<0.001	− 1.1	<0.001	− 0.74	<0.001
Syntactic	V14	− 0.06	0.812	0.2	0.423	1.53	<0.001	2.53	<0.001
	V15	− 0.07	0.822	0.06	0.839	− 0.15	0.59	− 0.25	0.385
	V16	0.02	0.966	− 0.02	0.96	− 0.35	0.363	− 2.56	<0.001
	V17	0.59	0.123	0.21	0.569	− 0.86	0.031	− 2.31	<0.001
	V18	− 0.04	0.125	0	0.924	− 0.02	0.384	− 0.01	0.781
Symbolic	V9	− 0.32	0.156	0.48	0.014	1.26	<0.001	1.95	<0.001
Symbolic	V10	− 0.02	0.818	0.05	0.615	0	0.993	0.41	<0.001
Participation	V11	0.27	0.01	0.54	<0.001	0.54	<0.001	0.22	0.019
Participation	V13	0.01	0.954	− 0.02	0.929	0.07	0.725	0.35	0.102
Complementary information	V12	0.2	0.2	0.21	0.158	− 0.11	0.427	− 1.91	<0.001
Residual deviance: 92807.86
AIC: 92989.86