Hello, my name is Stephen, and this is my first post on the MAS.700 blog! I have to admit, usually when I invoke phrases like “the future of news” and “the future of journalism” it’s with a grain (or a fistful) of sarcasm. Nevertheless, I am very excited for this class and look forward to blogging here for the rest of the semester.

For our first assignment, we had to track all of our media consumption for a week and figure out a way to measure and present this information. Makes sense — to understand the future of news, we must first understand the present, most of all ourselves. “Know thyself, and you will know the universe.” Errr, of media. Read on!

The method to this madness

Like many others in the class, I resorted to using RescueTime as my primary mechanism for tracking my media consumption. Given the focus of the class, I also supplemented RescueTime with a manually-curated Google spreadsheet of any news content I was consuming. In any measurement-based observation, it’s important to understand the limitations of the tools you’re using to make the measurements. In the case of RescueTime:

1. RescueTime is good for tracking your time usage on a single device, but when you introduce other devices in addition to your PC, there can be difficulty measuring media consumption across all platforms. While there exists an Android app, there are no solutions for my iPhone (which, luckily, was out of battery for most of the period) or my Xbox. Neither is there a good way to keep track of media consumed in communal spaces, such as TV shows in my hall lounge. RescueTime also failed to properly log time when I had my laptop plugged into my larger monitor, (I suspect) because the application was not “focused” on the correct display.
2. RescueTime can create weekly reports of your time usage, but the week can only start on Sunday or Monday according to the dashboard. Since we started this assignment in the middle of the week, this led to a very annoying issue where you could only see your weekly reports for 2/3 – 2/9 and 2/10 – 2/16 individually, with no way to specify a custom start and endpoint (for the purposes of this experiment, 2/6 – 2/11). In other words, there was no real way to see the data in aggregate — as a workaround, some of the class looked at the two weeks separately, focused on only one, or did a day-by-day breakdown.

I managed to solve both these problems by using the 14-day free trial for a RescueTime Pro account. This gives you two perks: you can manually enter “offline” data, which I used as a workaround to enter data on my secondary devices or data that failed to log properly. Secondly, you can export your data as .csv files, thus allowing me to merge data from the two weekly reports using OpenRefine.

As for my supplementary Google spreadsheet, I opted for a relatively broad definition of news content, listing any news story, article, or blog post that I read (but excluding social media posts simply due to the sheer number and difficulty of measurement). For each piece, I made a note of when I read it, what the source was, and how I was referred to the link (direct navigation in the browser, Facebook, Twitter, Reddit, etc).

Small data

With those methodological choices in mind, let’s delve into the data. As it logs your computer usage, RescueTime tries to predict what category a particular site or application falls into, with varying degrees of accuracy. Based on this categorization it also tags time as being “productive” or “unproductive”, which is a rather lazy approach to measuring productivity, as it assumes that each site (or category of sites) has a single, fixed mode of interaction, which is hardly the case. Unless otherwise specified, RescueTime automatically tags News & Opinion content as being “very unproductive.” As a result, I chose not to look at the productivity data and instead looked generally at the category breakdown:

Of the 50 hours I logged this past week…

• 18.6% of my time was spent playing video games
• 16.6% of my time was spent watching videos, TV, or movies (YouTube, Netflix, offline)
• 16.5% of my time was spent doing or reading about software development
• 10.2% of my time was spent on social networks (Facebook, Twitter via Tweetbot)
• 8.9% of my time was spent logging/reviewing the data (RescueTime, Google Docs – the observer effect for this experiment)
• 7.3% of my time was spent reading the news/blogs
• 4.9% of my time was spent on my email

Overwhelmingly, I spend my time on entertainment media — along with music (not pictured), it occupies a combined total of 37.3% of my time. Sublime Text 2, my preferred text editor, and localhost, my machine’s local testing server, logged significantly higher numbers than they would have on a normal week — over the weekend, I participated in the 2014 QUILTBAG Jam and built a game within a day, skewing the numbers immensely. In other words, I’m even less productive than this data suggests. If we exclude these two items and Google Docs, which was used as a data-logging tool, the worst offenders were:

1. Hearthstone (Blizzard’s online trading card game — think Magic: The Gathering)
2. Facebook
3. Twitter (Tweetbot)
4. Gmail

Interestingly enough, all these things have one thing in common: they have a very short use cycle. Hearthstone features quick games that typically take 5-10 minutes, making it the perfect way to take a quick break from work or fill the small void of time before a class. As a Facebook user, I like to pull up the news feed every now and again via the easy two-letter fb.com domain name. I quickly flick through it to look for things that pique my interest, and that’s it.

I use Twitter in very much the same way, except I check Tweetbot more often as it’s a desktop application (though, apparently, for shorter periods of time than Facebook). Email tends to work the same way — I’ll check it once in a while, resolve any important emails, gif threads, or flame wars, and then quit out. All four of these things take up small units of time per use cycle, and yet over a week’s time, quickly snowball into the biggest recipients of my time and attention.

Breaking (down) the news

Finally, onto the Google Spreadsheet data. I decided to look at two main things: the news sources that I was reading, and how I getting linked to those articles. I don’t really have any favorite publications that I exhaustively read — The Tech, maybe, but the amount of content is so low that I would hardly consider this exhaustive.

Instead, most of my news reading comes in via links on social networks and is largely dependent on the ebbs and flows of the week as well as the particular time at which I check those social networks. As a result, most of the 83 news articles I read were unique to their source (i.e. that was the only article I read from that website). Looking at the sites that managed to get multiple hits, we see the following results:

Taking a step back from the top news sources, we can see which referral methods were most frequent overall. Facebook and Twitter dominated the pack — for future study, it’d be interesting to see how these referrals cascade down from Twitter (where I follow more journalists/news nerds) to Facebook (more of my IRL friends) and how these two frequencies might fluctuate depending on what time(s) of day I was looking at each network. If, for example, I’m not checking Twitter when a particular story is published and getting shared, I may see it later in the day if/when it propagates through my Facebook friend network.

Moreover, there is a stark contrast between the number of articles I directly navigated to in the browser or hit via inlinks and the number of articles I accessed via outlinks. What this says is that I hardly click into external links or source links in articles (or perhaps it’s a testament to the fact that people aren’t providing these links to retain traffic?) I almost never use search to find news stories (in the one case shown here, it was a reference piece, not a news story). Unsurprisingly, the social stream remains the supreme decider of what articles I do or don’t read — thus explaining why everyone’s so keen on trying to dominate it.

There are two more interesting statistics based on my news consumption for the week. First, I wanted to see the percentage of listicle content I was reading and the sources/referrals for them. I’m pleased to announce that only 4 of the 83 articles I read this week fell into this bucket (naturally, from BuzzFeed, Mashable, and Gizmodo, all referred via Facebook), making my news diet only 4.8% listicle.

Secondly, I wanted to see how I really latched onto a single story (as I mentioned in class last week, it was previously Philip Seymour Hoffman’s death) and read every article I could about it. This week, that story was the meteoric rise and fall of Flappy Bird, a game that seemingly came out of nowhere and invaded the public consciousness even faster than doge meme.

This week, 8 out of the 83 articles I read were exclusively about Flappy Bird. That’s nearly 10%. Far less than I would have expected, given how often the game was referenced in my conversations, email/comment threads, and my Twitter feed. Still, it’s a nontrivial portion of my news consumption, and perhaps points to the reasons why more and more news outlets (most prominently The Atlantic, but Flappy Bird was covered everywhere from CNN to Forbes) are doing more and more of these critical/longform pieces dissecting pop/meme culture. It’s a particular subgenre of online journalism that my brother and I experimented with when we ran 21st Century Boy, and definitely one that I will continue to keep my eye on as we progress through this course.