10 May 2011

Cross-spherical analysis of the representation of human suffering in the Web


Another research using digital methods:

Introduction

On March 11, 2011 more than 8.000 people died in Japan and more than 12.000 people are still missing because of the earthquake, and tsunami (Rijksoverheid, 2011). December 2010 until January 2011, Australia was hit by a series of floods which affected more than 200.000 people, and killed 35 people in Queensland (Queensland Police Service, 2011). January 12, 2010, up to three million people were affected by the earthquake in the Haitian capital (CBS News, 2011), and at least 217.000 people died (BBC News, 2011). All these natural disasters destroyed recently many cities, and made even more people suffering from diverse kinds of pain, e.g. pain of injuries, pain of loss, etc. However, when we think of suffering people, we have a picture of starving African people in our heads or Indian children living in slums. This assumption becomes evident when you type the keywords “human suffering” into the search bar of Google images. Once you hit the enter key, you will see mostly starving people from under-developed countries. One reason for this ‘postmodern subjectivity’, as Yasmin Ibrahim (2010) calls it, is the “everyday and non-stop consumption of images” (ibid.) of suffering people from under-developed countries in humanitarian campaigns, such as UNICEF.
This subjectivity can lead to ‘compassion fatigue’, a phenomenon in which “people become so used to spectacle of dreadful events, misery or suffering that we stop noticing them and are left unmoved.” (Tester, 2001, p.13). Also Chouliaraki (2008) claims that the the “overdose of misery [...] renders suffering banal, unimportant and irrelevant to the spectator’s lifeworld; each piece of news on suffering is yet another story that reaches our screen, only to disappear in oblivion as soon as we zap to another channel.” (p.373). But not only television can be blamed for the portrayal of suffering. The new mass medium, the Internet, and print media, e.g. urban billboards, are part, as well, of the criticism of the ‘mediatization of humanitarian action’ (Boltanski, 2000). Hereby, the media does not solely use ‘shock effect’ images, but also ‘positive images’ which can lead equally to a compassion fatigue because these picture can be misrecognized as “these are not children in need” or “everything is already taken care of” (Chouliaraki, 2010). That does not mean that humanitarian action itself has always negative effects on the Western collective imagery, but the represented and the mediated idea of pity in public genres has shaped this imagery “by connecting the public figure of the citizen to the figure of a spectator who contemplates upon and feels for a distant sufferer.” (Chouliaraki, 2006, p.265; Arendt, 1990). Boltanski (2000) writes even more critically that “the representation of the suffering that goes together with a portrayal of humanitarian action, especially on television, is intrinsically bad because it transforms the spectator into a voyeur, stimulating his perverse desire to take pleasure in the suffering of others or, at best, provoking feelings of shame for not being able to assuage the suffering that is being shown.” (p.5). That means that the target of criticism is “not humanitarian action as such, but rather its representation - particularly its representation in the media.” (ibid., p.4). But the question here is, does the Web and the online news really portray more suffering of under-developed countries than developed ones, like Japan or Australia where we can find suffering people, too, due to natural disasters?
This research tries to find out whether the most under-developed countries, the most represented countries in the Web and the news are referring human suffering. Or, whether recent catastrophes change the depiction of suffering. Therefore the ‘geography’ of human suffering will be examined by using digital research methods that will scrape two spheres: the newssphere and the websphere. This "cross-spherical analysis compares the sources returned by each sphere for the same query. It can therefore be seen as comparative ranking research. The digital methods approach may also may be called comparative source distance analysis.” (Digital Methods Initiative, 2011).

Questions

Which country is the most prominent one according to the Web, and the news talking about human suffering?
Are there differences between the spheres according to the hierarchy of ‘suffering’ countries?

Method

Choosing the key words:
In order to scrape the newssphere and the websphere, it is important to chose proper and clear key words which should clarify the representation of suffering. According to the online Cambridge dictionary suffering is defined as a noun that occurs “when you experience physical or mental pain” (Cambridge Advanced Learner's Dictionary, 2011). The Visual Thesaurus displays the verb ‘to suffer’ in the following semantic field:

Image 1: Visual display of the verb ‘to suffer’ (Visual Thesaurus, 2011)

This image shows that suffering is related to get, have, endure, bear, etc. pain, distress, misfortune, misery, woe etc. But all these words are too specific for this case, i.e. this research requires a general or broad term that implies different kinds of pain, distress, etc. and that is widely used in the English speaking countries. For example, looking at the search results of ‘suffering’ and its synonyms (Apple Dictionary, 2011) in table 1, it is obvious that ‘suffering’ is the most used word.

Table 1: Search results of ‘suffering’ and its synonyms using the Australian Google domain (google.com.au), retrieved on April 7, 2011

Because ‘suffering’ is widely used and does not imply a certain form of pain - it can be either physical or mental pain - it is chosen as the main key word for further analysis. Beyond that, the word ‘human’ will be added to make sure that the form of suffering is related to human pain and not for instance to animals. Hence, the key words are ‘human suffering’ in order to examine the geography of suffering.

Making a list of countries:
The list of the chosen countries comes from one main source: the 2010 Rankings of the ‘Human Development Index’ (HDI) of the United Nations Development Program's (UNDP) Human Development Report. This report was released on November 4, 2010 and presents the HDI of 169 countries. The HDI compares the following factors in order to measure the quality of life: life expectancy, literacy, education, and standards of living, i.e. well-being (especially child welfare). After comparing these measures, it is possible to categorize the 169 countries and to distinguish between developed, developing and under-developed countries.
The most under-developed countries, as stated in the report, are: Zimbabwe (0.140), Democratic Republic of Congo (0.239), Niger (0.261), Burundi (0.282), and Mozambique (0.284). The number in the brackets show the HDI (the lower the number, the lower the quality of life according to the UNDP). As you can see, the lowest ranked countries are all from the African continent. Hence, countries from other continents were added to get a broader overview of the geography of suffering. These additional countries are: Haiti, representing the American continents with the lowest HDI of 0.404. Afghanistan (0.349), Bangladesh (0.469), India (0.519), and Japan (0.884) were added as well, as a representative of the Asian continent. Afghanistan, Bangladesh and India are analyzed due to their low HDI, in contrast to Japan. Japan is here included because of the natural disasters (earthquake and tsunami) that happened recently. So, this research expects that Japan will be highly mentioned within the list of suffering countries in the Web and the news, even though it is a developed country. Australia, the second highest ranked country of the Human Development Report with a HDI of 0.937, was chosen in order to test whether countries are more represented in their own news websites and Google domain (google.com.au) than other/foreign countries. That means, does Australia has the most search results related to their own country or is it more reporting about other countries? In conclusion, the following eleven countries are examined within this cross-spherical analysis: Zimbabwe, Democratic Republic of Congo, Niger, Burundi, Mozambique, Haiti, Afghanistan, Bangladesh, India, Japan, and Australia.
Another indicator of the quality of life within a country that should be mentioned, is the ‘Human Suffering Index’. This index is defined by the Population Crisis Committee (Washington, DC) and similar to the used HDI. The index uses “ten measure of human welfare related to economics, demography, health, and governance: income, inflation, demand for new jobs, urban population pressures, infant mortality, nutrition, access to clean water, energy use, adult literacy, and personal freedom.” (Population Crisis Committee, 2011). However, this index was not utilized because it was not possible to find any reports or statistics on the Internet of several countries, i.e. the Population Crisis Committee does not provide the public with extensive information about the Human Suffering Index.

Cross-spherical analysis:
In order to study the websphere and the newssphere, the Google domain of Australia (.com.au) was chosen. That means, that both the Australian Web and the Australian news were queried with the search term “human suffering”. Even though, Norway is the highest ranked country of the Human Development Report, Australia was selected because it is an English speaking country, i.e. the researcher is not familiar with the Norwegian language.

1. Querying the websphere using Google Web search
a. The top hundred results for the search term “human suffering” using Google Web search were collected and pasted into the Harvester tool in order to get the extracted URLs from the search engine results. Hereby all news-related, word-defining and Youtube-related URLs were excluded from the top hundred search results (see appendix for a complete list of URLs).
b. The top fifty8 URLs of returns for the query “human suffering” were then copied and pasted into the top text box of the Googlescraper (Lippmannian Device) tool. The list of the key words and the chosen countries were placed (one per line) into the bottom text box of the tool in the following way: “human suffering” + country x.
c. After using the Googlescraper, a ranking of the countries by number of mentions by single URLs in the top fifty is presented for the comparison with other spheres.

2. Querying the newssphere using Google News search
a. The top hundred results for the search term “human suffering” using Google News search were collected and pasted into the Harvester tool in order to get the extracted URLs from the search engine results. Hereby all not news-related URLs were excluded from the top hundred search results (see appendix for a complete list of URLs).
b. These top fifty URLs of returns for the query “human suffering” were then copied and pasted into the top text box of Googlescraper (Lippmannian Device) tool. The list of the key words and the chosen countries were placed (one per line) into the bottom text box of the tool in the following way: “human suffering” + country x.
c. After using the Googlescraper, a ranking of the countries by number of mentions by single URLs in the top fifty is presented for the comparison with other spheres.

NB: Only the top 50 of the 100 returned websites were used for this research because not all websites contained one of the chosen countries in combination with the key words. These links were, thus, removed from the list and replaced with the following URL from the list. Thereby, it was assured that each website found at least one of the eleven countries.

Findings

After querying the web and newssphere, the following search results were returned (see table 2 and the two given Excel sheets for a detailed and complete overview of all links and search results).

Table 2: Maximum number of results per query and per sphere in alphabetical order

Hereby, it is necessary to mention that the maximum number of results per query of the Googlescraper is 1000. So, if a website mentioned one country more than 1000 times, then the number 1000 was written in the Excel table because it is not possible to record more than 1000 search results within one website.

Google News results:
The ranking of the eleven countries, starting with the most mentioned and ending with the least mentioned country within the 50 news-related websites. Ranking of the newssphere:
  1. Japan
  2. Afghanistan
  3. India
  4. Australia
  5. Haiti
  6. D.R. Congo
  7. Zimbabwe
  8. Bangladesh
  9. Mozambique
  10. Niger
  11. Burundi
These results show that there is a prominent country associated with human suffering, viz. Japan. The least mentioned country is Burundi which was only mentioned 65 times out of 15.462 search results.

Google Web results:
The ranking of the eleven countries, starting with the most mentioned and ending with the least mentioned country within the 50 websites. Ranking of the websphere:
  1. Japan
  2. India
  3. Australia
  4. Afghanistan
  5. Haiti
  6. Zimbabwe
  7. Bangladesh
  8. D.R. Congo
  9. Mozambique
  10. Burundi
  11. Niger
These results show that there is a prominent country associated with human suffering, viz. Japan. The least mentioned country is Niger which was only mentioned 61 times, but closely followed by Burundi (63) and Mozambique (67).

Cross-spherical analysis:
Both spheres show more or less conformity of their search results. The ‘suffering’ country Japan was in all spheres the most important one. Also India, Afghanistan, Australia, and Haiti had nearly similar ranking positions on the top of the scale. Whereas, Burundi, Niger, and Mozambique were one of the least prominent countries, i.e. both times in the end of the ranking.
Further it is visible that the newssphere returns more than twice as much search results (15.462) than the websphere (6739). But surprisingly, the least mentioned countries were all around 60 within both spheres.

Results & Discussion

The findings of the web and the newssphere show an interesting depiction of human suffering. Namely, on the top of both rankings we can see the developed country Japan which was recently extremely mentioned in the news because of the natural disaster that happened in March, 2011. Also Australia, a highly developed country as well, is located in the first half (3rd and 4th position) of both rankings. In contrast, we can find the least developed countries (Burundi, Niger, and Mozambique) in the last positions of both rankings.
These outcomes demonstrate that the geography of suffering countries is on the one hand affected by present happenings, such as natural disasters. That means that the three countries, where lately a catastrophe has happened (Japan, Australia, and Haiti), returned one of the most search results. On the other hand, the news-related websites deliver the most content about human suffering in certain countries. This shows that humanitarian actions from several foundations and organizations, such as UNICEF, are not the biggest suppliers of such content. Mostly digital newspaper report about suffering countries. However, it is also visible that the representation is not significantly influenced by the Google domain. This research expected that Australia would be higher ranked because of the usage of the Australian Google domain. However, I suppose that Australia would have been more often mentioned, if only Australian websites and online newspapers were used in this research. Also the number of dead and injured people could be an indicator for the amount of search results within the spheres. Hereby, were the Australian citizens not as much affected as the population of Haiti or Japan. These are just assumptions and should be studied in further and more detailed research.
Further, I assume that Afghanistan is highly ranked (2nd and 4th position) because of the still ongoing (2001 until present) war against the United States. That means, that Afghanistan is constantly in the news, and thus, the Internet has already collected many articles and reports about the local situation.
All in all, it is possible to conclude, based on this research, that the under-developed countries are not as much represented in the Web, as supposed in the beginning. Except of India, are all other suffering countries less mentioned than the developed countries. We can see, as well, that all African countries are the lowest ranked countries compared to the other chosen ones. But in order to get a more detailed list of suffering countries, it is necessary to choose more countries of each continent, which could be done in further studies. It is also recommendable to choose for different key words in order to get an overview of the reasons of suffering – is it because of starvation, poverty, natural disaster, etc.? Other continuing research questions could be: which medium (Internet, television, print-media, etc.) shows more information about human suffering? Which factors are responsible for the depiction of human suffering (e.g. poverty, amount of dead people, amount of personal affection)? How is it possible to avoid compassion fatigue but still ask for humanitarian action?

Literature

Arendt, H. (1990). On Revolution. London: Penguin Books.

Boltanski, L. (2000). The Legitimacy of Humanitarian Actions and their Media Representation: The Case of France. Ethical Perspectives, 7(1), 3-16.

Chouliaraki, L. (2008). The mediation of suffering and the vision of a cosmopolitan public. Television & New Media, 9(5), 371-391. DOI: 10.1177/1527476408315496

Chouliaraki, L. (2010). Post-humanitarianism: Humanitarian communication beyond a politics of pity. International Journal of Cultural Studies, 13(2), 107–126. DOI: 10.1177/1367877909356720

Chouliaraki, L. (2006). The aestheticization of suffering on television. Visual Communication, 5(3), 261-285. DOI: 10.1177/1470357206068455

Ibrahim, Y. (2010). Distant suffering and postmodern subjectivity: The communal politics of pity. Nebula, 7(1/2), 122-135.

Tester, K. (2001). Compassion, Morality and the Media. Buckingham: Open University Press.