Class Projects - Fall 2014


Chrome extension that monitors an individual’s time spent online

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What it does

Our product, Slice, is a chrome extension that monitors an individual’s time spent online. The idea behind our extension stemmed from a book written by Clay Johnson: The Information Diet. The book concluded that if an individual wants to become more aware of or alter their online activity, they should follow the similar steps as someone who wishes to become more aware of or alter their food intake: monitor it. With that, our chrome extension was built in a previous class, and we refined it.

We believe that our idea is meant exactly for the type of people that The Information Diet describes the people wishing to become more aware of or alter their online activity. As an added bonus, we’ve included a social media functionality, via Twitter, to our extension so that users can choose to share this information as well. In its most recent version, our project has two separate screens: an online activity screen and a reading screen.

The online activity screen allows an individual to view all of their time spent online, divided into the following categories: Streaming, Email, Social Media, Shopping and Reading. The reading screen expands upon the reading category, showing whether an individual spends more time reading about politics, or one of the other categories shown above in the screenshot. Below the pie charts shown for each screen, users may view in detail where the data that populated those pie charts came from. For example, to understand why their data may be more skewed towards the “shopping” category, they will be able to see what links they visited, and for how much time, that were categorized as shopping.

How it works

Our application starts by reading the domain of the website that is currently active. Then, it checks that domain against a number of user defined arrays. If it is not in one of the arrays, it is a reading site and the domain is sent to the topic categorizer. If it is in an array we assign the category manually. After getting the category, we get back other metrics from the categorizer and place the information into the proper section of the pie chart data. Then, we use the pie chart data to make a pie chart and display it in the extension that the user sees.

Key Technologies:

  • Kango
  • Categorizer
  • Javascript
  • D3

Next Steps

Given that Slice was the future work of a previous project, we do believe that there is always room for improvement. We hope that in the future, users will be able to share their pie charts across multiple social media sites, which will be able to analyze and compare users’ pie charts to one another.

Additionally, we hope that future rendition of our extension will be able to sort through emails, without intruding on privacy, in order to categorize whether a user spends more time looking at Spam emails, work emails etc. Lastly, we believe that our extension can be further refined to be able to identify what type of news articles one is reading (i.e. if the article is more democratic or conservative). We are confident that the extension is set up such that it is simple to build upon, and hope that it heads in this positive direction.


Student Team:

Neha Govindraj

Sean BowenWilliams

Jonah Ruffer

Juliusz Choinski

Faculty Guidance:

Larry Birnbaum

Rich Gordon