Large scale analysis of violent death count in daily newspapers to quantify bias and censorship

Journal of Big Data

Table 3 Spectral index for the four newspapers separating domestic and foreign datasets

Newspaper	Loc.	\(\gamma _L\)	\(\gamma _H\)	\(\Delta \gamma \gamma _L-\gamma _H\)	\(\chi ^2 /NDF\)	M (excess/defect) (%)
NYT	Dom.	1.37e+00 ± 3e−03	1.78 ± 1e−02	−0.417 ± 1e−02	3e + 01	4
NYT	For.	9.90e−01 ± 3e−03	1.66 ± 5e−03	−0.672 ± 5e−03	6e + 01	− 98
CDS	Dom.	2.08e+00 ± 3e−03	1.81 ± 2e−02	0.277 ± 2e−02	2e + 01	71
CDS	For.	5.02e−01 ± 3e−03	1.59 ± 5e−03	−1.09 ± 6e−03	1e + 02	− 122
REP	Dom.	1.69e+00 ± 3e−03	1.54 ± 6e−03	0.143 ± 7e−03	2e + 02	16
REP	For.	9.66e−01 ± 3e−03	1.48 ± 4e−03	−0.511 ± 5e−03	1e + 02	− 91
STA	Dom.	1.95e+00 ± 2e−03	1.72 ± 1e−02	0.2311 ± 1e−02	4e + 01	47
STA	For.	9.23e−01 ± 3e−03	1.54 ± 5e−03	−0.615 ± 6e−03	9e + 01	− 94

\(\gamma _L=\gamma (2\le k \le 10)\), \(\gamma _H=\gamma (10\le k \le 10^6)\). A negative \(\Delta \gamma =\gamma _L-\gamma _H\) implies a lack of events for \(2\le k\le 10\) events, and vice-versa. The higher the absolute value the higher the excess or defect of events. M is the excess (+)/defect (−) of articles with respect to what expected from a pure power-law (see text)