Celebrity profiling through linguistic analysis of digital social networks

Computational Social Networks

Table 17 Model of the person’s characteristic Occupation

Group Features	Variable	Manager		Performer		Politics		Professional
	Predictor	Estimator	P-value	Estimator	P-value	Estimator	P-value	Estimator	P-value
Lexical	(Intercept)	− 4.7	<0.001	6.68	<0.001	− 12.13	<0.001	− 7.87	<0.001
	V1	0.02	0.298	− 0.11	<0.001	0.17	<0.001	0.06	<0.001
	V3	1.85	0.369	− 7.95	<0.001	− 9.86	<0.001	− 1.49	0.662
	V4	0.88	<0.001	− 1.25	<0.001	2.93	<0.001	1.21	<0.001
Syntactic	V14	− 0.9	0.005	1.3	<0.001	− 0.5	0.038	− 0.58	0.113
	V15	0.88	0.013	2.22	<0.001	− 1.92	<0.001	0.69	0.093
	V16	− 3.89	<0.001	− 2.58	<0.001	− 7.42	<0.001	− 3.89	<0.001
	V17	4.43	<0.001	− 0.64	0.111	5.07	<0.001	4.8	<0.001
	V18	− 0.12	0.004	− 0.1	<0.001	− 0.23	<0.001	− 0.05	0.243
Symbolic	V9	0.22	0.193	0.66	<0.001	− 0.92	<0.001	0.29	0.157
Symbolic	V10	0.63	<0.001	0.55	<0.001	1.25	<0.001	0.75	<0.001
Participation	V11	0.12	0.265	0.03	0.544	− 0.24	0.006	0.22	0.07
Participation	V13	− 0.16	0.546	− 0.45	<0.001	1.5	<0.001	− 0.37	0.226
Complementary information	V12	− 0.91	<0.001	− 0.15	0.091	− 2.69	<0.001	− 0.36	0.117
Residual deviance: 100703.9
AIC: 100913.9