London: On Wikipedia, there is a greater tendency to cover flooding events in wealthy countries rather than in poor countries, says a study. The results suggest that Wikipedia editors must make a greater effort to cover disasters suffered by the neediest countries.
By performing careful, large-scale analysis of automatic content, “we show how flood coverage in Wikipedia leans towards wealthy, English-speaking countries, particularly the USA and Canada”, the researchers claimed in their work.
The study conducted by researchers from Universitat Pompeu Fabra (UPF) in Barcelona, Spain, is scheduled to be presented at the 17th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2020), Virginia Tech in Blacksburg, Virginia (USA), from May 24-27.
Valerio Lorini (JRC-UPF), Javier Rando (UPF), Diego Saez-Trumper (Wikimedia) and Carlos Castillo (UPF) are the authors of the study.
“We also note that the coverage of flooding in low income countries and in countries in South America, is substantially less than the coverage of flooding in middle-income countries”, they added.
For this research, the authors estimated the coverage of floods in Wikipedia taking many variables into account: gross domestic product (GDP), gross national income (GNI), geographical location, the number of English speakers, fatalities and various indices describing the country’s level of vulnerability.
The researchers analysed 458 events that had been reliably described as floods, according to the records of two or three sources of reliable data: Europe’s Floodlist; the United Nations’ Emergency Events Database (EM-DAT), and the Dartmouth Flood Observatory (DFO) of the University of California (USA).
They compared these data with the entries in Wikipedia to locate these events and see if they were consistent or not with the data sources contrasted in terms of location and time references.
“The results of our analysis are consistent over several dimensions, and draw a box where Wikipedia coverage is biased towards some countries, particularly the most industrialised and where large settlements are English speaking, and at the expense of other countries, particularly lower income, more vulnerable ones”, the authors suggested.
The results show that the tools that use data from social networks or collaborative platforms should be carefully evaluated to avoid bias. These results correspond only to one possible type of natural disaster, floods, but other types of events could also be considered for study.
