Introduction

Social networking sites provide large amounts of information about their members and the networks to which they belong.  This information can be an interesting source to be used for different kinds of research. ' Localized' information, especially, is increasingly important.  The aim of this research is to create a method for localizing social networks in Google Maps based on publicly available information.  This will show the whereabouts of the members of a specific network.  Different networks visualized in Google Maps can then be compared. 

Sample Study

Mapping a Hyves-network in Google Maps. 

Research question: Where are Geert Wilders' Hyves friends located? 

Method

In Hyves.nl: all members of the Geert Wilders-hyve are scraped for URL and place-name. 

The table has to be converted to a KML file which can be read by Google Maps. 

The Google Map visualizes the the number of members per place-name of the Geert Wilders-hyve. 

Data

Url's - place/city - coordinates: Friends of Geert Wilders (manually transcribed from the first pages of the Geert Wilders-hyve). [ note: adding a place-name to a hyve-profile is not compulsory.  Some profiles do not have a place-name attached to it and therefore have not been taken into account in this table.] 

Result: Google Map

Result: Tagcloud

Another option for visualizing the results is a tagcloud.  But, as can be seen above, a tagcloud of place- or city names might not be representative.  For example, in the Geert Wilders-hyve we found that relatively more (at first glance) members are from small towns, rather than the Randstad.  Many of these small towns will probably only have a few members, so they will not appear to be important in the tagcloud.  In the tagcloud, the Randstad is still going to appear as the most important location for Geert Wilders-friends.  The Google Map however, will show that there are relatively more members from small town places outside the Randstad. 

Visualizations in Google Maps using a KML script

Or you click on the link above to see the source code of the KML file. 

Tool proposal

Steps for scrape

In order to scrape a Hyve network on Hyves.nl, it is necessary to log in with a Hyves-account.  Otherwise place-names are not shown. 

The Hyve of a certain person, group or issue is manually searched and selected on Hyves.nl.  This is necessary because there can be several 'unoffical' sites related to a subject or person that are not the object of study.  For example, there are twelve differt 'Geert Wilders'-hyves, some of other people with the same name, some 'unofficial' of the politician.  Selection of the Hyve to be used for the scrape should therefore be done manually. 

The URL of the specific Hyve is necessary in order to start the scrape. ( For instance, URL of Geert Wilders-hyve: http://wilderspvv.hyves.nl/)

Below this the coordinates are filled in to put the placemarkers in the wanted place on the map.  Note: the coordinates are filled in in reversed order.  So for example the coordinates of Vlaardingen are reversedDus bijvoorbeeld bij de uitkomst van Vlaardingen worden de getallen omgedraaid: 51.909874, 4.341996 wordt 4.341996, 51.909874. 

Research suggestions

Question: Where are the people related to a certain issue, such as 'animal suffering', located? 

Creating the image of a city/country by means of photos.  The image associated to many countries are certain objects, like The Netherlands to tulips, Paris to the Eiffeltower, etc.  Used website: Locr.com.  Research into the most frequently used tags connected to specific countries.  Do the photo's match to the tags that are attached to them?  Are photos with geotags generally placed by local inhabitants, or by tourists placing their holiday-pictures?  In what way does this influence the image created of a country?