In November 2014, we held our second hackathon with our YRS participants. This event was much anticipated as it gave us an opportunity to return all of the data we had been collecting over a six month period via the MobileMiner app. The day began with Giles doing a brief presentation, outlining what we had collected and how our young coders could best work with all the information that they were given.
While there were some initial struggles in trying to figure out what to do with all of the data, our researcher participants persevered and came up with some brilliant prototypes for apps and ways to visualize our data. What we quickly realized is that there are just so many things one can do with a lot data, all you need is a good imagination!
In just a few hours, here are some of the hacks that were undertaken by our research participants:
- Cell Tower Time: Featured an app that tried to display different colours on a map to show what cell towers where used at certain times. This decision was made instead of using GPS, because our researchers collectively decided that this form of location tracking would have been too invasive for our participants. As a result, the MobileMiner app converted cell tower data into very approximate location data using the gazetteer of cell tower locations provided by opencellid.org. Had this hack been completed, it would have highlighted all of those Cell Tower IDs that had been gathered during the six months of data gathering, highlighting how easy it is for us to be tracked with the most basic kinds of technology.
- Data Mining: This hack created applications using the SQLlite databases that were created in the MobileMiner application. This collaborative effort tried to produce something that might allow users to gain insight into what the ‘average’ leaks when the engage on social media platforms. In other words, thinking about ways of quantifying the notifications that occur when a user uses an app such as Facebook or Twitter.
- Social Media and the Days of the Week Correlations: This hack tried to develop a graph generating tool to demonstrate when and for how long, the Twitter, Facebook and Facebook Messenger app were used by our research participants. More specifically, it aimed to know what days of the week these apps were being used, alongside the number of times they were accessed and at what time of the day they were most frequently used. He then cross referenced this with the number of notifications that the apps sent back to their home servers. By so doing, he asked three questions of the data: 1) Does using an application result in an increased number notifications? 2) Is there a day of the week that appears to make the user more vulnerable to the app leaking? 3) Is there a time a day that makes the user more vulnerable to the app leaking? In addition, the hack tried to visualize social media use and seeing how this changes over time. From this perspective, the hack tried to extend the quantified self app to encompass social media usage.
- Sonification of Processes and Notifications (Sonosocialdata-ism): This hack tried to process sketch that takes the data from the mobile miner app (as a CSV file) and produces a tone at a higher frequency with processes (popularity of usage) and increases volume with notification count. The participant thus turned data into sound by developing a number of tones and colours to correlate with the frequency in which the apps we had been monitoring were calling home. Had this project been completed, then the app would have generated brighter and more grating sounds to coincide with the leakier and more invasive apps found on any user’s device. This unique approach to visualization came from their belief that sound and colour signify more clearly to the average user. In their own words: ‘Most people would prefer this to numbers on a screen or paper, as its a lot more jarring for the non-savvy, as some could say that higher, louder tones are uncomfortable, whereas seeing numbers is relatively meaningless unless you know the context…This could break down the complicatedness for the end user…It would be great if you could listen to a list of apps, to find the tones, to find the one that are potential problems.’