A well-established name

Protocol

Introduction

In this phase of our research we wanted to understand how the Internet talk about Net Neutrality. Debates are always difficult to be named, it’s hard to define a specific focus because of the number of actors and objects involved. The debate on Net Neutrality is, in this context, very particular: indeed this term clearly identifies the proper Internet controversy.

When we approached the theme for the first time, we tried to look at it from different points of view. But we found out that we didn’t need to define this debate through different terms, because it already had one: Net Neutrality.

The queries that we’ve chosen, with the idea that we wanted to obtain data as uniform as possible, are: “Net Neutrality”, “Open Internet” “Stand for”, “Open Internet” “Against”, “Two speed Internet”, “Pay to play Internet”.

Step #1

We searched the five queries on Google.com with an incognito window, collected the first 100 results and extracted all the urls with pgl.yoyo.org. Once we cleaned them by removing all the duplicates links, the pdf files and the corrupted links, we tagged the remaining links in “News”, “Blog”, “Activism”, “Petition”, “Politics”, “ISPs”, “CP”.

Step #2 — news and blog speak the same

Most of the data that we collected were related to the first two categories, so we decided to analyze and compare firstly them. For each queries we took “News” and “Blog” links and extracted their .txt files (with Zup). By keeping queries and categories separated we analyzed those text files (with Sven). Once we’ve refined the results of the terms frequency by joining similar words, we selected the top 20 most interesting terms to compare and understand if News and Blog speak the same or not.

Step #3 — many terms, one meaning

Then we created a second corpus by putting together the results of all the queries to analyze all the other categories. We took the links tagged like “Activism”, “Petition”, “Politics”, “ISPs”, “CP”, extracted their .txt files (with Zup) and analyzed them (with Sven). Once we’ve refined the results of the terms frequency by joining similar words, we selected the top 20 most interesting terms to find out in which way those categories were different.

Metadata

Timestamp:
6/12/2014 - 7/12/2014

Data source:
Google Search

Tools:
Zup, Sven