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Motivation
What comes to your mind when you think about the most dangerous jobs in the world? The other day I was talking to a friend about high-risk professions. She told me journalism would be a risky one. At first I laughed at her, since she’s a journalist. However, after she give me some supporting clues I decided to conduct a little research on this. Eventually, I found the Committee to Protect Journalists – CPJ that maintain a good dataset taking records of injured, imprisoned and killed journalists and media workers around the globe. I realized that many journalists are killed every year while covering everything from business and sports to revolutions, wars, political upheavals, elections, corruption, human rights violations etc.
Data
The CPJ began documenting the deaths of media workers in 1992, as a way to drive attention and recognition of the vital role these individuals play in newsgathering. Journalists are defined by CPJ as people who cover news or comment on public affairs through any media, including in print, in photographs, on radio, on television, and online. The database takes up cases involving staff journalists, freelancers, stringers, bloggers, and citizen journalists. The clean spreadsheet I’m using in this analysis can be acessed from my Github data repo. But a more updated version can be downloaded here.
Results
Reported deaths over time
In 1992, the number of deaths reported was 55, and 74 in 2017. The highest number is seen for 2007.
The sex of journalists
Only a fraction of the journalists killed is female (7.1%). As we don’t know the balance of female/male in the profession across countries, it will be difficult to evaluate this statistic. It could be that male journalists are more likely to be sent risky zones.
jornalists %>% dplyr::filter(!is.na(Sex))%>%
dplyr::count(Sex) %>%
dplyr::mutate(Freq = n / sum(n, na.rm=TRUE)) %>%
ggplot(aes(x = Sex, y=Freq)) + geom_bar(stat = "identity") +
theme_flex()
The nationality
jornalists %>% dplyr::filter(!is.na(Nationality))%>%
dplyr::count(Nationality) %>%
dplyr::mutate(Freq = n / sum(n, na.rm=TRUE)) %>%
dplyr::arrange(desc(n)) ## # A tibble: 95 x 3
## Nationality n Freq
## <chr> <int> <dbl>
## 1 Iraq 161 0.140
## 2 Syria 105 0.0910
## 3 Philippines 80 0.0693
## 4 Pakistan 59 0.0511
## 5 Algeria 58 0.0503
## 6 Russia 54 0.0468
## 7 Somalia 53 0.0459
## 8 Colombia 49 0.0425
## 9 India 46 0.0399
## 10 Mexico 42 0.0364
## # ... with 85 more rowsThe organization
As we can see from the table below, .11 or 11% of the reported deaths reffers to people working as a freelancer jornalist. Al-Arabiya television comes in second, but very far below from the freelancer position.
tab1 = jornalists %>% dplyr::count(Organization)
jornalists$Organization <- ifelse(grepl(pattern = "freelance", jornalists$Organization, perl = FALSE), "Freelance", jornalists$Organization)
jornalists$Organization <- ifelse(grepl(pattern = "Freelancer", jornalists$Organization, perl = FALSE), "Freelance", jornalists$Organization)
# jornalists <- jornalists %>% dplyr::mutate(Organization = ifelse(grepl("freelan", Organization), "Freelance", Organization))
tab2 = jornalists %>% dplyr::count(Organization)
jornalists %>% dplyr::filter(!is.na(Organization))%>%
dplyr::count(Organization) %>%
dplyr::mutate(Freq = n / sum(n, na.rm=TRUE)) %>%
dplyr::arrange(desc(n))## # A tibble: 1,271 x 3
## Organization n Freq
## <chr> <int> <dbl>
## 1 Freelance 211 0.112
## 2 Al-Arabiya 15 0.00797
## 3 Reuters 14 0.00744
## 4 Al-Shaabiya 12 0.00638
## 5 Al-Iraqiya 11 0.00584
## 6 Baghdad TV 9 0.00478
## 7 Al-Jazeera 8 0.00425
## 8 Al-Sharqiya 8 0.00425
## 9 Algerian State Television 8 0.00425
## 10 BBC 8 0.00425
## # ... with 1,261 more rowsMedium
What is the most frequent medium of the killed journalists?
jornalists$Medium <- ifelse(grepl(pattern = "Print,Internet", jornalists$Medium, perl = FALSE), "Several", jornalists$Medium)
jornalists$Medium <- ifelse(grepl(pattern = "Television,Internet", jornalists$Medium, perl = FALSE), "Several", jornalists$Medium)
jornalists$Medium <- ifelse(grepl(pattern = "Radio,Television", jornalists$Medium, perl = FALSE), "Several", jornalists$Medium)
jornalists$Medium <- ifelse(grepl(pattern = "Print,Television", jornalists$Medium, perl = FALSE), "Several", jornalists$Medium)
jornalists$Medium <- ifelse(grepl(pattern = "Print,Radio", jornalists$Medium, perl = FALSE), "Several", jornalists$Medium)
jornalists$Medium <- ifelse(grepl(pattern = "Radio,Internet", jornalists$Medium, perl = FALSE), "Several", jornalists$Medium)
jornalists$Medium <- ifelse(grepl(pattern = "Internet,Television", jornalists$Medium, perl = FALSE), "Several", jornalists$Medium)
jornalists %>% dplyr::filter(!is.na(Medium)) %>%
dplyr::count(Medium) %>%
dplyr::mutate(Freq = n / sum(n, na.rm=TRUE)) %>%
dplyr::arrange(desc(n)) %>%
ggplot(aes(x = reorder(Medium, -n), y=n)) + geom_bar(stat = "identity") +
labs(y = "Number of deaths ", x = "Medium Type", title= "Reported deaths by medium type") +
theme_flex()
Job
The Most Dangerous Job in Journalism Is Just Being a Reporter in
jornalists %>% dplyr::filter(!is.na(Job)) %>%
dplyr::count(Job) %>%
dplyr::mutate(Freq = n / sum(n, na.rm=TRUE)) %>%
ggplot(aes(x = Job, y=n)) + geom_bar(stat = "identity") +
theme_flex()
The type of coverage/episode
jornalists %>% dplyr::filter(!is.na(Coverage)) %>%
dplyr::count(Coverage) %>%
dplyr::mutate(freq = n / sum(n, na.rm=TRUE)) %>%
ggplot(aes(x = Coverage, y=n)) + geom_bar(stat = "identity") +
theme_flex()
The type of death
CPJ applies strict journalistic standards when investigating a death. One important aspect is determining whether a death was work-related or not. The case will be considered “confirmed” only if there is reasonably certain that a journalist was murdered in direct reprisal for his or her work; was killed in crossfire during combat situations; or was killed while carrying out a dangerous assignment such as coverage of a street protest. Journalists who are killed in accidents such as car or plane crashes are therefore not included in the dataset. However, when the motive is unclear, but it is possible that a journalist was killed because of his or her work, CPJ classifies the case as “unconfirmed”, but the investigation can continue.
jornalists %>% dplyr::filter(!is.na(`Type of Death`)) %>%
dplyr::count(`Type of Death`) %>%
dplyr::mutate(freq = n / sum(n, na.rm=TRUE)) %>%
dplyr::arrange(desc(n)) %>%
ungroup() %>%
ggplot(aes(x = reorder(`Type of Death`,-n), y=n)) + geom_bar(stat = "identity") +
labs(y = "Number of deaths ", x = "Type of Death", title= "Reported deaths by type") +
theme_flex()
The country that kills the most
jornalists %>% dplyr::filter(!is.na(`Country Killed`)) %>%
dplyr::count(`Country Killed`) %>%
dplyr::mutate(freq = n / sum(n, na.rm=TRUE)) %>%
dplyr::arrange(desc(n))## # A tibble: 108 x 3
## `Country Killed` n freq
## <chr> <int> <dbl>
## 1 Iraq 278 0.148
## 2 Philippines 138 0.0733
## 3 Syria 131 0.0696
## 4 Mexico 99 0.0526
## 5 Pakistan 89 0.0473
## 6 Colombia 85 0.0452
## 7 Russia 82 0.0436
## 8 India 75 0.0399
## 9 Somalia 71 0.0377
## 10 Algeria 61 0.0324
## # ... with 98 more rowsThe source of fire
jornalists %>% dplyr::filter(!is.na(`Source of Fire`)) %>%
dplyr::count(`Source of Fire`) %>%
dplyr::mutate(freq = n / sum(n, na.rm=TRUE)) %>%
dplyr::arrange(desc(n))## # A tibble: 23 x 3
## `Source of Fire` n freq
## <chr> <int> <dbl>
## 1 Political Group 412 0.322
## 2 Military Officials 231 0.180
## 3 Unknown Fire 210 0.164
## 4 Government Officials 189 0.148
## 5 Criminal Group 115 0.0898
## 6 Paramilitary Group 58 0.0453
## 7 Local Residents 26 0.0203
## 8 Mob Violence 14 0.0109
## 9 Military Officials, Political Group 5 0.00391
## 10 Criminal Group, Government Officials 4 0.00312
## # ... with 13 more rowsFor additional information, including the list of the journalists killed in 2012, visit:
In 2012 alone, 103 journalists were killed around the globe. Motives were confirmed for 70 of them. The deadliest countries for journalists in 2012 were Syria (28 deaths), Somalia (12 deaths), Pakistan (7 deaths), and Brazil (4 deaths). The motives where the confirmed in these cases.
The way journalists are killed, range from crossfire or combat to murder. Impunity is a shocking 100% for murder cases. More detail in the chart below:
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