My Quantified Media Diet
I produced a 12 page book summarizing my findings. It is a limited edition of 200 copies with some of the charts above and more. It was printed on a risograph and looks really amazing. Order a copy of the book here.
Since 2010, I have been tracking and measuring different aspects of my music listening habits. In 2018, inspired by Steven Soderberg’s Seen/Read tracking and Jason Kottke’s Media Diet posts, I branched out beyond music to track all of the media I consumed. A picture of my media intake would provide context for understanding music’s place in my life.
I hatched a plan that would stitch together a number of sources to aggregate a number of media types. Using the Hours app and manual notes, I tracked all the podcasts, TV/movies, and books I consumed throughout the year. Using Last.fm, I tracked my music listening. And for the all the reading I did online, I used Pocket app plus IFTTT to track each article.
In total, I spent 74 days, 17 hours and 7 minutes consuming media. That amounts to 20.45% of all the time there was in 2018. That averages out to 4 hours and 54 minutes per day, of which 48% of my time was spent listening while 43.5% was spent watching. The remainder was filled in with reading. Adding in my average of 2 hours and 38 minutes of phone time per day (some of which is accounted for in my media total), I still clock in well below the average American (according to a Nielsen study most American’s spend over 11 hours a day consuming media). Regardless of where I rank versus the average American, it still feels like too much. 1/5 of my time being occupied with or distracted by media.
Finding the total amount of media I consumed was only part of the goal. Learning more about my media consumption habits was the other part. A full analysis of what I found lives forever in a printed report I produced, chock full of numbers and charts and graphs. In the meantime, here are 5 of my favorite charts and insights from that report.
1. Podcasts vs Music
I listen to podcasts while commuting to work and running in the mornings. Those two behaviors are apparent when plotting my podcast listening by time of day. My morning weekday routines are appear consistent and rigid while the evening routines are more fluid. On the weekends, any semblance of routine dissolves. My music listening fills in the daytime hours while working. It’s more prominent in the afternoons than mornings. It reappears late in the evening during the week.
2. All Media by Time of Day
A broader look at all media types reveals additional patterns. Watching things is clearly an end of day activity that spills out into the day on the weekends. Reading things is as sporadic as listening to music, filling in little gaps of time here and there. However its presence is more dominant throughout the day, especially in the mornings.
3. Media by Topic
Those charts do a good job of illustrating when. With some analysis of my data, I was able to derive the topic of each piece of content to see the what. No matter the medium, sports was the most prominent topic. I went a layer deeper and tried to understand which sports get the most attention and in which form of media.
A few things stand out. First, it’s clear I like watching basketball the most and I watch fairly consistently throughout the season and then more frequently in the playoffs. Reading about basketball spans the entire year, even picking up in frequency during the offseason. Other sports, like the Olympics or Tour de France, have watching and listening spikes during the event. But reading doesn’t seem to play a part in following those sports. Football and baseball have a somewhat regular hum of reading that appears to be independent from watching.
4. Effects on Consumption
Using additional data points, I was able to explore a few different things I thought might effect my overall media consumption. While the sample size was small, traveling had the most significant impact. The second largest effect was changing jobs. Both listening and watching were higher at my previous job compared to my new job. I noticed a slight decline in media consumption when the weather was hot, driven largely by a decrease in watching content.
5. “Songs Played After the Most Played Song” chart
My most played song of the year was COME MEH WAY by Sudan Archives with 31 total plays. I looked at the next nine songs played after that song, thinking it might give me a snapshot of my listening behavior.
I found that there were 29 different groups of songs. One particular listing path includes the same 4 songs in a row followed by branching into 7 different paths. Those four songs, plus the most played song, were, at the time, the most played songs by Sudan Archives on Spotify. After those songs played, the auto-play algorithm took over and served up random songs. It is insightful to see the different contexts in which I listened to this specific song. Of the 217 songs played after COME MEH WAY, 87 of them were Sudan Archives songs. Evidence that I am an artist/album listener as much as a playlist listener.
I continue to do these projects not knowing what will come out on the other side. When I start unpacking the data I then remember why I do it. Personal data is a mirror for our lives. And even though the data may be specific, we should still see ourselves reflected in it. That holds true with this project. I can see myself reflected back in the data. And new facets of who I am have a sharper focus.
As mentioned at the top, I produced a 12 page book summarizing my findings. It is a limited edition of 200 copies with some of the charts above and more. It was printed on a risograph and looks really amazing. Order a copy of the book here.
Previous year’s projects are compiled on my website: www.ericboam.com