Description
After analyzing the google queries about “gender in/equality” in the previous visualizations, we tried to give form to the whole phenomenon, as treated on Wikipedia. The aim was to get the representation of how articles are related to each other, which topics are most relevant according to Wikipedia in relation to both “Gender Equality” and “Gender Inequality”, and which meaning it creates to Wikipedia users, what are the common topics between Gender Equality and Gender Inequality and what is the major debate in comparison to google search results. We used the Seealsology tool to examine the network of the connections between the most related topics to both “Gender Equality” and “Gender Inequality”. The network is intricate and works on many levels. To be most effective and relevant, we focused on level one.
Furthering our Wikipedia research, we compared the table of contents of both the ‘Gender Equality’ page and the ‘Gender Inequality’ page. It is important to note, that in our visualization we compared the contents by topic as opposed to by title. We connected the contents from each page with the parallel on the other that included similar information, provided there was one, regardless of the specific title.
This led us to question why these two pages are not merged given their similarity and Wikipedias commitment to merging multiple pages on the same topic. The online discussion from Wikipedia made us aware that they are categorized separately because ‘Gender Inequality’ is considered a long-standing sociological phenomenon, with roots in history and biology for example with influences on every aspect of human life. However, ‘Gender Equality’ is a singular but widely proposed goal to eradicated injustices - it looks to the future for a solution, but it is only one of many proposed solutions.
To give us a more complete overview of ‘Gender Equality & Inequality’ online we decided to merge our results from the two main platforms we focused on, Google and Wikipedia. We compared our manually created Google clusters, from the previous keywords analisys, to level one of the generated Seealsology Wikipedia network. By doing this we discovered that discrimination and progress are the most linked and popular topics discussed online, not only work place gender inequality as our initial first stage of research suggested. By following these steps we have been able to create a deeper investigation and less superficial analysis of the ‘Gender Equality & Inequality’ debate online.
Protocol
First, we used both “Gender Equality” and “Gender Inequality” together in Seealsology tool, and by extracting the graph we defined the network of the topics in relation with both queries. We focused on the pages from the level one of the network, as they are only one step from the “Gender Equality” and “Gender Inequality” Wikipedia pages.
Then we linked the list of Wikipedia pages from level one to the manualclusters found in the chapter analyzing the keywords from 200 Google articles from our two querys.
Data
Timestamp: 01/12/2016 - 05/12/2016
Data source: Scopus, Google Scholar
Download data (4MB)
We have found the list of Wikipedia “Gender Equality” and “Gender Inequality” pages and compared the dataset from see Chapter 2.