In tracking my media usage for the week, I gained something that I imagine most people gain when engaging in this exercise: anxiety. Anxiety and paranoia that I have stopped paying attention to what media I was being exposed to / exposing myself to, that there were aspects of my media consumption that I was significantly less aware of, or that I was generally unconscious of the majority of my consumption on a daily basis. In other words, it worked.

My strategy for designing my media journal was not simply to find out how often I was accessing media, but to develop an ontology for engaging with media and test it to see what properties of media access were the most revelatory about my habits. What follows is a breakout by each of those properties, some of which are revelatory, and some of which might benefit from collecting over a longer timeline.

First, I categorized my media consumption by what it was about. One thing about recording this was that it drew attention to how frequently I was consuming more than one form of media at the same time. Obviously the largest category, music, was mostly consumed while also engaging with a number of the others. It is no surprise to me that work and social are among the biggest categories, but it was surprising just how large of a percentage was dedicated to art (and Instagram, which I struggled to categorize given that it is a platform with multiple types of content; I went with “social;art”, since my primary use for it is to follow artists and designers).

One of the aspects of the journaling I was most interested in was how much media consumption was a choice vs. forced on me by context and environment. Admittedly, I am likely to have dramatically underreported the media I was involuntarily exposed to. Reflecting on walking through the city, it already occurs to me that e.g. I stood in front of a number of advertisements on the back wall of the subway platform that I was subconsciously aware of, but which didn’t rise to the level of conscious consumption. That said, I do thing what I realized from this process is that, for the media I am at least partially engaged with, most of it is quite purposeful. Non-discretionary is listed, as some media, such as presentations, or readings for classwork were voluntary, but not optional/assigned by others. (This chart is based on number of engagements, not amount of time spent on each piece of media. If this were based on time, it would look dramatically different, skewing toward nondiscrentionary.)

Media type gives a bit more resolution in terms of what I was consuming. What was surprising for me was the variety. If I was to imagine the various types of media I was engaging with on a daily basis, I would have guessed perhaps only 3-4, but it appears there is still diversity in the ways I consume media. Again, advertisements are not broken out here, which might have been interesting. A stand-out is the “platform” category, which represents types such as Twitter, Instagram, Instant Messaging platforms, etc. bringing into focus the amount of times I engage with media in an ecosystem where I am likely to be exposed to many other types of content.

I tracked what channels the media I consumed came to me from. No surprise that I’m the top culprit here in terms of choosing to expose myself to media. Community, friends, and classes are about on equal footing, but on a long enough timeline, I’d be curious to see how this actually played out. My suspicion is that class would spike and the influence of my friends or online communities would stay mostly the same. (I recently purposefully locked myself out of Facebook and handed the keys to a trusted friend, so it was an interesting time for me to journal. I shudder to think what these charts would look like if my usual habits of being tempted into admittedly a lot of good, yet likely superfluous content.)

The amount of social media content here is alarming, even without Facebook. I think that if I spent more time counting the various exposures during class time (when I appropriately wasn’t diverting my attention to log every item) this would balance out with social, or at least that’s what I’m going to tell myself…

In terms of what devices this media is experienced through, I would have expected that “laptop” would have dominated my phone accesses much more. Another comparison I’m going to continue to keep my eye on. Again, this is not based on time spent on engaging with the media, so this chart would likely skew toward “laptop” given the amount of times I use it to read long-form items, which I can’t stomach via my phone. At the same time, if I added up every one of the micro-engagements I had on my phone, it’s possible the gap would be smaller than I’m imagining.

In general, I think the voluntary/involuntary comparison and source of media analyses were the most educational in understanding my own habits of consumption. No doubt, even tracking a handful of metrics for a short time period heightened my conscious awareness of the beginning of an interaction with a given form of media dramatically.

I did something a little different for my “media” diary. Rather than track what “goes into my head” via digital media, I tracked what goes into my head — and out of it — via non-digital conversation. 

Why would I do such a thing?

1. I’m really interested in exploring the work conversation does to unite us, teach us, and help us co-create knowledge and understanding, now that it’s so easy for people to speak and be heard.
2. I’m convinced that journalists could gain a lot from guiding and even leading public conversations around the news wherever they happen — as could society at large.
3. And as for my focus on tracking my non-digital, rather than digital, conversation: Sometimes a cool way to learn what something is is to look at what it isn’t. Digital conversation gets criticized — hastily, I think — for being less immersive, building less empathy, etc., than non-digital conversation. To the extent that’s true, what can we do to bring more of the strengths of in-person conversation to the digital world? Taking a close look at our own conversations might help us figure that out.

So here’s what I did, and what I learned:

Tracking non-digital “conversations”

Here’s one of the 9 sheets I used to track, by hand, each and every non-digital conversation I had over three days

For the purposes of my tracking, I defined a “conversation” as an exchange among myself and at least one other person in which we all use our actual voices to speak. So in-person and telephone conversations are in, but tweets and emails are out.

For each and every separate conversation I documented over the waking hours of three days (Feb. 11, Feb. 12 and Feb. 15), I tracked a bunch of stuff:

• duration (in minutes)
• location
• whether we talked to each other in person or not (y/n)
• whether the conversation was specifically scheduled (y/n)
• how well I know the other participants (0-5, with 0 = not at all and 5 = they’re family)
• whether I knew the participant’s names

And these are key:

• how immersed I was in the conversation (0-5, with 0 = barely paying attention and 5 = nothing else matters)
• how much I felt the conversation built on the relationship between myself and whoever I was speaking to (0-5, with 0 = not at all and 5 = hugely bonded)
• how much I enjoyed the conversation (0-5, with 0 = not at all and 5 = tremendously)
• how much enduring knowledge I got from the conversation  (0-5, with 0 = none and 5 = tons)

I collected information on 169 separate conversations.

And here’s what I got, by the numbers…

Most of my conversations were under 5 minutes long. The longest conversations happened while people were sitting still: a meeting over coffee, another over lunch, a dinner with friends, and catching up with my husband at the end of the day.

THE BASICS

• 42 percent of the time I was awake, I was in conversation
• The average conversation lasted 6.6 minutes
• Half my conversations clocked in at under a minute, while only 10 percent lasted 30 minutes or more. Those shorter conversations went by fast, though: They accounted for just 0.3 percent of the total time I spent in conversation
• 1 of every 10 of my conversations were with strangers whose names I didn’t know (waiters, cashiers, etc.). But they, too, were quick, making up just 0.7 percent of my total conversation time
• I spent 516 minutes — 44 percent of my total conversation time — talking to just the three members of my immediate family: my husband, my 3-year-old son, and my baby daughter. Unsurprisingly, a third of my conversations took place at home.
• All but 12 of these non-digital conversations happened in person. Eleven were phone conversations, and one was an online podcast interview

CONVERSATION AT WORK

I was extra curious about the three scales on which I measured the work my conversations were doing — relationship building, enjoyment, and knowledge building — and to what extent they were related to a conversation’s duration, or how immersed I felt I was in each one.

These were subjective, self-reported scales, and fairly uncalibrated. But just for fun, here’s the lowdown:

• I spent 70 percent of my conversation time in discussions I really enjoyed, in 37 exchanges I rated a 4 or 5 on the enjoyment scale
• I spent 15 percent of my conversation time in discussions that significantly built on my relationships, in just five exchanges I rated a 4 or 5 on the relationship scale, all of whom I also rated a 4 or 5 on enjoyment
• I spent 24 percent of my conversation time in discussions where I felt I learned a significant amount, rated 4 or 5 on the knowledge building scale. Only six conversations met this high bar — and five of these six were also rated 5 on the enjoyment scale
• For reference: On average, I rated my conversations a 2.6 on immersion, a 1.6 on relationship building, a 2.5 on enjoyment and a 0.4 on knowledge building

Now for the fun part. Did longer conversations correlate with higher grades on these scales? How about conversations in which I felt more immersed in the conversation itself, with less preoccupying or distracting me?

Here are a few charts to illustrate an answer to those questions. I claim no statistical significance here, but you can see some trends — especially when you compare immersion with enjoyment…

SOME TAKEAWAYS:

Just from a walking-around-in-my-own-body standpoint, there’s no question to me, having done this, that I get more out of conversations when I’m able to focus on them, and on the people I’m talking to. That’s really hard, though, and the rewards of great conversations are more rare than I realized.

I do a lot of other things while I talk to people. Walking around, taking notes, eating, looking something up on my phone … Many of my conversations fit in the transitional periods between other activities.

But many of my most enjoyable, longest lasting and knowledge building conversations stayed still somehow. A lunch with a student who inspires me. A one-hour reading lesson with my son. A family dinner at my best friend’s place.

Here’s another interesting tidbit: Of the 169 conversations I tracked over these three days, 15 had been formally scheduled to happen (coffee meetings, a lunch, an interview, etc.). Those I rated, on average, 4.1 on immersion, 2.9 on relationship building, 4.2 on enjoyment and 2.5 on knowledge building — far higher than the total conversation averages listed above.

The attention economy, indeed…

This post is about two things: 1) The arbitrariness of the ways in RescueTime (and I!) define media and 2) the realization that I really need to spend far, far less time looking at screens. (Far, far less.)

I used RescueTime to track my media consumption, but then threw that data into buckets. A few attempts to wrestle through some of the questions I found interesting, below.

News, Entertainment & Social

This first rough slice looks at three categories: the time I spent on news, social and entertainment. I used these categories because RescueTime uses them, but I already see ample space for confusion and critique. We’re already playing in somewhat arbitrary territory, in the sense that the time I spent on BuzzFeed got classified as news consumption, but could possibly have gone into entertainment (was it a listicle? Was the listicle newsworthy?). Meanwhile, BBC America went into news, but I’m pretty sure I spent those 11 minutes looking at Doctor Who action figures. On a more substantive note: there is genuine slippage between categories, here. For example, the “Entertainment” category includes all of YouTube (I didn’t track each video separately, but I almost wish I had). Part of that YouTube time was definitely informational videos about gravitational waves, and part of it was “Last Week Tonight,” a product that easily belongs in more than one category. Meanwhile, most obviously, “Social” is both news and entertainment. In an era when I’ll see Tweets about an earthquake before it appears in the news, I’m genuinely getting breaking news off social media (never mind the more hyperlocal ‘news’ about my friends’ lives.) Finally: let’s stop pretending the news and entertainment are separate. I don’t think that helps anyone.

News, a Breakdown by Source

 I found this breakdown interesting. Here I’ve broken down “news” by point of origin, excluding social (ie, FB). The big green slice towards the bottom is blogs. This includes Medium and WordPress sites, and they dwarf any other individual news site. Also, I don’t know what killed news brand loyalty (search? social?) but it’s clearly dead.

Users Versus Big Media 

 In the news and entertainment categories, I thought it would be interesting to look at media based upon who produced it. This is where it all got interesting. The clunky term “user-generated” (which still roams at large in many newsrooms) is both off-putting and, it turns out, not very useful. The “User-generated” slice here includes everything I watched on YouTube. When I think about the stuff I watch you YT, it includes a ton of Youtube cover bands, makeup tutorials (deal with it), and comedy groups. My point: I spend a lot of time consuming media on YouTube that is produced by neither Big Media nor casual users, but by some in-between professional class of producers whose only distribution mechanism is YouTube. Some of these folks are quite famous, some of them make a comfortable living off what they do on YouTube. But they’re not part of the group of producers we consider Big Media. Meanwhile, User-gen also includes Reddit, which is some strange and uncomfortable hybrid of user-generated and big media.

User-gen, breakdown

This chart demonstrates the weirdness of “user-generated” as a term, still further. I’ve got content from Reddit, Blogs (Medium, Medium’s Matter publication, etc) and YouTube all jumbled together in here. None of this is produced by ‘users’ in isolation – even platforms like Reddit and YouTube enjoy massive monopolies over particular types of interaction, and they are hardly small players. They provide development, editing and interaction tools on their platforms, thereby participating in the process of content creation. Therefore, the final product, even on a site like Reddit, is partnership-generated. This content isn’t generated by professional journalists, but we (professional journalists) might be the only people who care about that distinction anymore.

News vs. Telegram

This chart looks depressing, but it’s actually possibly vaguely heartening.  Remember up top, when I said I need to spend less time with screens? That’s true, certainly, but it isn’t true that screen time =/= relationship time. In this chart, I juxtapose all the time I spent on news this week against the time I spent on Telegram, the social networking site I use the most. Here’s the interesting part: I only use Telegram to talk to a handful of friends. That means that I spent 6 hours and 52 minutes talking to approximately four friends, online. In a way, this is an homage to relationship-building, isn’t it? Even when I’m online, the #1 thing I’m doing is maintaining relationships with a core group of friends. The most interesting part of all this is that these are the four friends I probably hang out with the most in daily life, too. Interesting? Weird? Cult-ish? Maybe a bit of all three, but at least I feel a little less like an antisocial nutjob.

Print vs. Online

Unfortunately, I can’t find the exact quote, but a few months ago a fairly senior official at a big news publisher made headlines (Tweetlines?) by suggesting that young people will one day get tired of consuming news online and will go back to print newspapers. Whoever he is, wherever he is, this chart is for him.

Video vs. the World

 This is another one of those charts I kinda struggled with, but it gets at a distinction I wanted to address. Because I’ve been using RescueTime for a while, very little of what I’ve discovered this week surprises me. That said, I was reminded just how much time I spend watching video content online. (And it’s interesting, because despite overwhelming evidence to the contrary, I still think of myself as a ‘reader’.) What’s interesting is that this statistic might actually underestimate the amount of time I spend watching video, because it doesn’t count the time I spent on, say, a Vox media page watching an embedded video on that page. (‘Video’, here, only counts time on video-specific sites like YouTube or Netflix.) This also raises really interesting questions about video distribution. BuzzFeed makes a ton of money off video. Here’s a quote from an article about their financial statements:

“You have to remember,” Dempsey added, “that BuzzFeed doesn’t operate on any sort of subscription model, is growing at a significantly higher rate versus traditional media companies, and also is doing a lot in their original video content, which is largely viewed outside of BuzzFeed.com, almost making this part of the business more comparable to an original content company vs. digital media publication.”

Part of winning the video play, at least if you’re taking a page out of the BuzzFeed playbook (and who isn’t, these days?), is being seen on aggregators like YouTube and Netflix. How many news publishers are serious about building audience on YouTube (I know what you’re thinking: does John Oliver count?) Meanwhile, if you search Netflix for news, what do you get? Nothing at all.

Note: if the image quality on these charts is lame, I’ve got higher resolution pdfs I can share.

This assignment came at the perfect time – it’s a very very busy month and I really need to improve my media diet. The goal of my media diary is to determine a media consumption routine that is the most productive(towards dissertation research).

Diary:  see screenshots below or see interactive diary here

the key: to mimic a hand-drawn feel, I used hatch marks. The messier the mark, the less productive.

total days: 1 – 7, from last Wednesday, I went on a ill-timed vacation

productivity is highest in early – mid morning

overall I am not productive 🙁

I work at the lab or on the train

Parameters: The media I measured are what inputs I get, not what I make(those are tracked in a spreadsheet already).

Collection: After using rescuetime for a few days, I decided against it because it’s automatic recordings didn’t allow me to reflect on what I consumed. The method I found most helpful was to hand record as I go throughout the day. After the first few days, I adapted my notes into a google form(left) so that I can input directly from my phone into a standard format.

The format of my recording results in a spreadsheet with columns for date, time, media, productivity, who I am with, part of day, duration, place, and a short description.

Visualization: I chose this assignment to write a simple reusable visualization module and experiment with opening up visualizations to others on github. Inspired by “Dear Data”, I made a simple spreadsheet to sortable hatch marks visualization.

It is really still a work in progress.

Conclusions: I am pretty predicable. I look at Instagram and online shop throughout the day. I am most productive in the lab, on the train, and in the early mornings.

There is so much I want to do for this visualization. Changing the labels, adding more notes, increasing the clarity with some modifications, and using google spreadsheets directly instead of a downloaded spreadsheet. This repo I made for code in the project is working. (without the key panel)

the entered data, the input form

“Ha, I have to write about my media consumption for last week? Big Deal, I know what I spend my time on.” My impression went roughly in this line when I found out that I had to embark on this crazy self-monitoring endeavor. Little did I know that self-monitoring in this digital age can be strangely cathartic.

My journey started with installing RescueTime in my borrowed MacBook Pro. First warning sign came to me when RescueTime asked me, rather incredulously, if I was in my senses when I marked News and Opinion category as productive. “Really? Most people mark it as distracting” was the website’s wisdom. But what would an impersonal website know about our priorities, we know better right? But the power of perceived monitoring became apparent when I started navigating through my digital life.

Here are some of the insights I got from my three-day digital surveillance: I may have been devouring media, both traditional and social, in my earlier life as a journalist, but as a grad student for last two years, my media consumption went downhill and I may have retracted into my metaphorical cave filled with library books and articles. A.T. Mahan, Halford Mackinder, and other luminaries took the place of Media glitterati in my life. Consequently, the most amount of time I spend either online or offline is devoted to these readings. But self-monitoring proved that this could be a far cry from the truth. I do spend a fair amount of resources (both data and battery power) on both ‘productive activities like checking news sites and ‘distracting’ activities like listening to online music (we certainly have to be current with the Grammys right?) or checking TweetDeck ever-so-often.

It was revealing to me that when time became a precious commodity, Facebook quickly went into the back-burner but twitter remained in the focus. Another information I found about my browsing habits is that I tend to hop from one website to another. I start off with an interesting tweet or article and start reading all the related stories or the hyperlinks present in the stories. Hyperlinks can be distracting, and addictive as well…

While it was interesting to see that I was following a pattern and unknowingly was being led from one article to another, the most interesting aspect I found is the time slots that I am online.

I tend to squeeze my Digital time between the classes and other sacred Grad School rituals, like realtime socialization. While this pattern is most visible during the weekdays, I do operate on identifiable time slots during the weekends as well.

Before the start of monitoring I was of the impression that I was doing a lot of work and that as a grad student, I was under enough workload. It was only after I started monitoring my own activity that I realized there is always enough time, we just need to look deeper into our own time consumption.