Many interesting data visualizations have been published since the FCC released the data from the 1.1 million net neutrality comments they received. The release of the machine-readable bulk data file from the Open Internet docket allows journalists, researchers, and others to analyze the data and offer a clearer understanding of public opinion on net neutrality.
On Tuesday, the Knight Foundation and data analysis firm Quid released a cluster map of emergent themes from a sample of 250,000 comments, pictured here.
The first thing that might stand out is that all the themes from the sample appear to support net neutrality. The visualization was published in a blog on NPR today, which includes the explanation that all the comments from the sample in opposition of net neutrality came from form letters, and form letters were collapsed into a single node. Even so, the majority of these form letters were in support of net neutrality. Elisa Hu writes, “Taken with the entire body of comments sampled, there weren't enough unique or organic anti-net-neutrality comments to register on the map.”
Another notable result is the themes themselves. Why, according to the sample of comments, do people support net neutrality? Some themes focused on equal accessibility of content itself, such as “’fast lanes’ inhibit innovation” and “all content should be equally accessible.” These ideas are fundamental to the definition of net neutrality, and are supported by Title II reclassification. Other themes focused on the importance of an equal playing field for content creators, such as “a ‘pay-to-play’ system will harm the diversity of the Internet” and “need for equality in promoting the American dream”.
As the Knight Foundation and others have demonstrated, analysis and visualization of the FCC open data set can offer greater depth to the results of the net neutrality comment period—something useful for both the public and the FCC. This type of innovation is very much part of what the FCC Open Internet rules are designed to protect. If you find or create more analyses or visualizations, please tweet them to us @publicknowledge or email them to firstname.lastname@example.org so we can share the results with our followers.