We measure how the world's news covered people being harmed — and the help and hope within it.
544 of its 817 stories (67%) were about people being harmed, usually 48%. Kathmandu accounted for 435 of Nepal's 544 stories about harm, carried by 498 outlets. 386 of Kathmandu's 435 stories were about protests or strikes. Reports of arrests ran at about 8 times the usual rate (67 stories). Its coverage was about twice as angry as the world's.
286 of its 639 stories (45%) were about people being harmed, usually 18%. Doha accounted for 274 of Qatar's 286 stories about harm, carried by 399 outlets. Reports of people killed ran at about 5 times the usual rate (172 stories). Its coverage was about twice as angry as the world's.
94 of its 248 stories (38%) were about people being harmed, usually 29%. Reports of evacuations ran at more than ten times the usual rate (30 stories). Flooding was named in 46 of its stories about harm, more than ten times usual, with heavy rain and landslides. Its coverage was about one and a half times as grief-stricken as the world's.
48 of its 75 stories (64%) were about people being harmed, usually 36%. Baghdad accounted for 38 of Iraq's 48 stories about harm, carried by 122 outlets. Reports of abductions ran at about 4 times the usual rate (5 stories).
44 of its 60 stories (73%) were about people being harmed, usually 55%. Sanaa accounted for 39 of Yemen's 44 stories about harm, carried by 54 outlets. Reports of people injured ran at about twice the usual rate (25 stories). Its coverage was about one and a half times as fearful as the world's.
248 of its 914 stories (27%) were about people being harmed, usually 16%. 114 of Paris' 156 stories were about protests or strikes. Reports of arrests ran at more than ten times the usual rate (111 stories).
67 of its 170 stories (39%) were about people being harmed, usually 19%. Reports of people affected ran at about three times the usual rate (6 stories).
114 of its 362 stories (31%) were about people being harmed, usually 17%. Reports of people injured ran at about 4 times the usual rate (11 stories).
45 of its 158 stories (28%) were about people being harmed, usually 16%. Reports of people affected ran at about twice the usual rate (6 stories).
29 of its 85 stories (34%) were about people being harmed, usually 21%. Reports of people killed ran at about twice the usual rate (8 stories).
Also named far more than usual: drownings in Pakistan (18 of its stories, about 4 times its usual); outbreaks in Nigeria (18 of its stories, about three times its usual); storms in Australia (17 of its stories, about 4 times its usual); meningitis in UK (10 of its stories, about three times its usual).
Stories about people being harmed, in every 100 stories, the 30 days to 10 Sep. The big dot is this day; the shaded band is a normal day (the 30 days before it). Click a day to open it. Based on 16,561 stories about harm among 90,814 counted on 10 Sep.
For the third day running, the news turned to Nepal. Stories about people being harmed made up 18 in every 100 of the day's 90,814 stories, as usual. Mentions of flooding ran more than ten times their usual in Indonesia's news. On the side of help, reports of people freed or reunited ran at about one and a half times the usual rate. Nepal stood out: 67% of its news was about people being harmed, usually 48%.
Written by code from the day's numbers.
Countries with at least 50 stories that day.
Stories, not people.
Each country shaded by how much of its news was about people being harmed, against the world's 18 in 100 that day. Stronger colour: further above. A country with few stories is drawn closer to the world's. Click a country.
Stories, not people. Countries with at least 50 stories.
Of the stories about people being harmed, what part of life each was about. The mark is usual.
Stories that report it, not people: we count what reporters wrote, not what happened.
The words in the day's stories were softer than usual. Filled dot: today. Ring: usual.
From the words reporters used across the whole day, never one story. Fear is the weakest of the four measures. How?
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