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The Adjective Index

How each outlet wrote from 20 September to 27 September, calmest first. Measured over the articles we read in full from each outlet (at least five, each over 150 words).

Calmest this week: SBS News, with a median Niral Score of 9.8 and 18.1 describing adjectives per 1,000 words.

#OutletArticlesNiral Score (median)Change Adjectives per 1,000 wordsSentiment intensity Ours, and what came out
1SBS News789.8-1.3 18.1 4.5 7.8 −2
2ABC News61510.1-0.3 16.2 3.5 8.2 −1.9
3The Register12710.4-0.4 20.1 2.3 8.3 −2.1
47NEWS19511.1+0.3 15.7 5.7 7.6 −3.5
5TechCrunch12511.3+0.1 16.6 1.5 9.6 −1.7
6BBC News5611.3+0.7 21.3 3.9 8.5 −2.9
7The Guardian Australia43412.10 19.6 4.2 9.6 −2.5
8Ars Technica7313.3+0.8 23.4 2.5 10.2 −3.1
9NASA4415+0.5 29.3 1.8 12.7 −2.3
10Engadget18215.2+0.6 28.8 1.6 13 −2.1
11The Motley Fool Australia24716.7+0.3 31.5 3.3 13.7 −3

How to read it

Why Mundane Read is not a row in this table

It would come first every week, and that is the problem. Every row above is an outlet's own reporting. Ours would be their reporting with the loaded words taken out, so it wins by construction — scoring best on the number you edit text against is a tautology, not a finding, and a league table whose author is at the top is a table nobody should believe.

The last column is the honest version of the same question. It is the same article measured twice, so there is nothing to rig: it says what we took out of each outlet, and it is different for each of them.

This measures how articles were written, not whether they were right, and which stories an outlet chose shapes it: a week of sport reads differently from a week of politics. Method · Accuracy · Data (JSON, CC BY 4.0)