Algorithms and echo chambers in the world of learning

There has been lots in the news this past year about social media bias and echo chambers, which started gaining prominence when algorithms started meddling in your news feed. The major web companies collect a huge amount of data about you and in doing so are building a detailed profile comprising demographic data, likes and purchases and other data that has been captured and purchased. As you ‘like’ posts and pages, so the algorithm delivers similar content back to you. Your friends like certain things, or ‘people like you’ like certain things, and the algorithm delivers more of that content to you too. You search for and purchase certain things, and you get delivered content related to that. Maybe you even give away valuable data via an innocuous-looking Facebook quiz,  which is then sold to highest bidder and fed into yet more algorithms to target you with stuff you might ‘like’.

The resulting and widely-discussed ‘echo chamber’ means people seeing content that mostly just panders to their existing world view, whatever that may be. With increasing numbers of people now consuming news through social media alone, this results in people being less challenged, less exposed to other opinions and events,  with their views becoming ever more polarised and entrenched.

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The quantified cyclist: analysing Strava data using R

This post was edited on 09 May 2017 to add some clarity around authenticating with the Strava API.

In 2016 I made a commitment to myself to record every cycle ride I made. As both a leisure cyclist and cycle commuter, I was keen to know how far I rode in a year, what was the accumulated distance of my daily commute, what distance did I cover on my leisure rides. I already recorded my weekend rides in a phone app called Strava, so it was pretty easy to get into the habit of clicking a button on my phone every time I set off on a cycle commute too.

So as we ended 2016, my thoughts turned to what my end of year results would be in Strava. However, as it turned out the data presented to Strava users (shown below) is quite lightweight. It only provides a total distance cycled for the year, and while you can tag a ride as a ‘commute’, nothing is actually done with this data in the Strava interface and the end of year results are not split between commute and non-commute, for example.

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