I’ve watched a lot of YouTube this past month. That’s not a confession — it’s a data set. So I decided to actually track it: every session, every category, every hour of the day, cross-referenced against how much schoolwork I had going on at the same time. I wanted to know if my viewing habits were as “mindless” as I assumed, or if there was a pattern underneath them I’d never noticed.
What I found reframed the whole question. This isn’t really a story about how much I watch. It’s a story about when I watch, why I watch, and what that timing reveals about stress, autonomy, and habit.
Panel 1 — How I Actually Spend My YouTube Time
Before zooming into anything specific, I wanted the whole picture: where does the time actually go? Broken down by category over about a month, gaming and VTuber content immediately pull ahead of commentary/reaction and music — [X hours] and [X hours] respectively, versus [X hours] and [X hours].
That split isn’t surprising on its own. What it does is set the terms for the rest of the story: if two categories are carrying most of the weight, the next natural questions are when that time gets spent, and how deliberately.
Panel 2 — When Am I Actually Watching?
Total hours only tells you what I watch — not when. Breaking the same viewing down by time of day shows [it clustering heavily in the evening and late-night hours / spreading out more evenly than expected across the day] — [X hours] between [time range] alone.
That clustering matters for what comes later. If viewing concentrates in specific windows rather than spreading evenly across the day, it starts to look less like background noise and more like a scheduled decompression period — which is exactly the idea Panel 4 tests.
Panel 3 — Subscribed vs. Recommended
Here’s where I genuinely didn’t know the answer going in. Of all the time logged, [X%] came from channels I actively subscribe to, and [X%] came from videos the algorithm surfaced for me.
That ratio is really a question about autonomy: how much of my viewing is a choice I’m making, and how much is a choice being made for me? [If subscribed dominates: it turns out my viewing is more self-directed than the “algorithm rabbit hole” stereotype suggests.] [If recommended dominates: it turns out a majority of my hours are being shaped by a recommendation engine, not by me.] Either way, this number becomes the thread that Panel 7 picks back up at the very end.
Panel 4 — Watch Time vs. Academic Workload
The common assumption is that screen time rises when there’s nothing else to do — that it fills free time. So I plotted weekly watch hours against a rough measure of my school workload over the same stretch of weeks to see if that held up based on my average during the fall semester as that’s when the school energy kicks in.
It doesn’t. [The two lines move together, not apart — as workload increases, so does watch time, peaking in the same weeks.] Instead of watch time filling the gaps in a light week, it’s showing up heaviest in the weeks I had the least time to spare. That’s the pivot point of the whole story: this isn’t a story about free time. It’s starting to look like a story about stress.
Panel 5 — How Long Are My Viewing Sessions?
If watch time really is tracking stress rather than boredom, session length should say something too. Short clips suggest quick, distracted breaks; long sittings suggest something closer to deliberate escape or decompression.
The distribution skews toward [short sub-10-minute sessions / longer 30+ minute sessions], with [X%] of sessions falling in that range. That distinction matters because raw hours don’t distinguish between “I watched five 3-minute clips between assignments” and “I sat down for two hours.” [Short sessions read more like fragmented coping; long sessions read more like intentional, scheduled unwinding.]
Panel 6 — The Big Takeaway: Viewing Spikes Under Stress
This is the panel the whole story has been building toward. One week stands out well above the trend line — watch time jumps to [X hours], compared to a typical week of [X hours]. That’s not a random spike. That week lines up exactly with [finals / midterms / a specific deadline].
Put together with Panel 4, this is the real finding: my YouTube time isn’t filling empty hours, it’s absorbing stressed ones. The instinct to treat screen time as “wasted time” doesn’t hold up against the data — it reads much more like a coping mechanism than a lack of better options.
Panel 7 — Is My Viewing Intentional or Incidental?
So where does that leave the autonomy question from Panel 3? Closing the loop, [X%] of my viewing was something I actively sought out, and [X%] was passively recommended to me.
That ratio is the note I want to end on, because it’s the one thing in this whole story I actually have some control over. The stress-driven spike in Panel 6 isn’t something I can fully engineer away — school workload is what it is. But how much of my downtime is a deliberate choice versus an algorithm’s suggestion is something I can shift, if I decide the current ratio isn’t the one I want. That’s less a conclusion than an open question I’m planning to keep tracking.
Helene Vu 
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