How quickly does collective attention fade on Hacker News?
In 1885, Hermann Ebbinghaus published a monograph on human memory. By memorising nonsense syllables and testing himself at intervals, he discovered that forgetting follows a predictable curve: steep at first, then levelling off. The forgetting curve has since become one of the most replicated findings in psychology.
The internet has a collective memory too. When a story appears on a site like Hacker News, it attracts a burst of attention — comments, votes, shares — that decays over time. But how fast? Is there a universal forgetting curve for collective attention? And has it changed over the 18 years Hacker News has been running?
To find out, I collected 570 stories from Hacker News spanning 2007 to 2025 — 30 per year, each with at least 50 comments — and recorded the timestamp of every comment. In total: 289,174 comments, each a data point on when someone cared enough to respond.
For each story, I measured the time between the story's posting and each of its comments. Then I normalised and averaged across all 570 stories. The result is the aggregate forgetting curve of Hacker News — the average rate of commenting as a function of time since posting.
The curve drops steeply: nearly half of all comments arrive in the first 6 hours. By hour 24, 88% of the conversation is over. By hour 48, it's essentially done.
But the shape of the decay matters. Switch to the log-log view above. If forgetting
were exponential — like radioactive decay — the log-log plot would curve downward.
Instead, it's approximately straight. This is the signature of a power law:
attention decays as t−α rather than e−λt.
Why does this matter? An exponential decay has a sharp cutoff — after a few half-lives, the signal is essentially zero. A power law has a long tail: comments keep trickling in far longer than exponential decay would predict. The internet doesn't forget cleanly; it fades, but never quite to zero.
I fitted both an exponential model (A·e−λt) and a
power law model (A·t−α) to each story's comment
arrival rate. The power law provides a better fit for 82% of stories.
This is consistent with research on collective attention in other online platforms. Wu and Huberman (2007) found power-law decay in Digg story popularity. Lorenz-Spreen et al. (2019) showed accelerating dynamics of collective attention across multiple platforms. The long tail may arise because stories get re-shared, re-discovered via search engines, or linked from newer content.
The half-life is the simplest summary of how quickly attention fades: the time by which 50% of a story's comments have arrived. The median across all 570 stories is 6.1 hours. But the distribution has a heavy right tail — the mean is 72.5 hours, dragged upward by a few stories with extraordinarily long attention spans.
Most stories cluster between 3 and 10 hours. But the tail extends far: some stories accumulate comments over days, weeks, or even longer. These are often "Ask HN" or "Tell HN" threads, or stories about ongoing events that keep generating new discussion.
The popular narrative is that attention spans are shrinking — that social media has trained us to consume and discard faster than ever. Does the data support this?
The answer is surprising: no. Or rather, it was — and then it reversed.
In Hacker News's early years (2007–2008), the median half-life was about 10–11 hours. The community was small, the front page turned slowly, and stories lingered. Through the early 2010s, as the community grew and the ranking algorithm matured, attention shortened dramatically. By 2015, the median half-life had dropped to 3.9 hours — a 65% decline from 2007.
But then something changed. From 2015 onward, attention spans began lengthening again. By 2024–2025, the median half-life is back to ~10 hours. The first-hour concentration dropped from 13% in 2015 to just 4% in 2025. Stories are generating more spread-out discussion.
What happened? Several possible explanations:
Selection effect: Our sample takes the top 30 stories per year by points. As HN grew, the top stories got more comments, which mechanically spreads the distribution. A story with 2,000 comments simply takes longer to accumulate than one with 200.
Algorithmic changes: HN's ranking algorithm determines how long stories stay on the front page. If stories are given more time at the top, they'll naturally attract comments over a longer period.
Time-zone diversification: As HN's audience became more international, stories posted in US morning hours now attract comments from European and Asian readers in their own mornings, spreading the curve.
Content maturation: Technical and nuanced stories may generate more thoughtful, slower discussion than the quick-reaction news items that dominated the early 2010s.
Select any story to see its individual attention curve. Some burn bright and fast; others smoulder for days.
Ebbinghaus found that individual human memory decays roughly as a power law:
R = e−t/S, where R is retention and S is the
strength of the memory. Later researchers proposed R = (1 + βt)−ψ,
a power-law form, as a better fit.
The parallel with collective attention is striking. Our data shows that HN's collective memory also follows a power law, with a median exponent of about 1.3. The functional form is the same, even though the mechanism is completely different: Ebbinghaus measured synaptic decay in a single brain; we're measuring the waning of interest across hundreds of thousands of independent actors.
This convergence may not be a coincidence. Power-law forgetting arises naturally in systems with heterogeneous time scales. In individual memory, different neural pathways have different decay rates. In collective attention, different readers encounter the story at different times. The aggregate of many exponential decays with different rates produces a power law — a result known as the Jost equation in memory research and as a mixture of exponentials in statistics.
"The internet doesn't forget like a person forgets. A person's memory fades smoothly. The internet's attention is more like a crowd dispersing from a square — most leave quickly, but a few linger, and occasionally someone wanders back."
Three main results from this study:
1. Attention follows a power law, not an exponential. The long tail means that stories are never truly forgotten — they just become increasingly unlikely to attract new comments. This is consistent with the "burstiness" of human attention documented in Barabási (2005).
2. The median half-life is 6.1 hours. Half of a story's lifetime discussion happens in the first quarter of a day. By 24 hours, the conversation is 88% complete. The internet's collective attention span is measured in hours, not days.
3. Attention spans are not monotonically shrinking. After declining sharply from 2007 to 2015, the median half-life of HN stories has been increasing since. The "shrinking attention span" narrative, at least on Hacker News, is out of date.
search_by_date endpoint (capped at 1,000 per story). Decay models fitted by
OLS on binned data (hourly bins, first 72 hours). Half-life defined as the empirical median
of comment arrival times relative to story posting time.