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In a comparison of 602 DEV Community articles published near my own posts and carrying the same tag, 134 had at least one reaction (22.3%) and 60 had at least one comment (10.0%), according to my published counts. That is a local benchmark, not a rate for all DEV posts—and it puts a single post’s silence in perspective without explaining why it happened.
What I measured
I had published 14 articles on DEV Community. Across those posts, I had received no reactions and three comments. Eight were old enough for their view counters to have caught up; those eight showed 103 page views in total. I used those eight posts for the comparison and left out the six newer ones because their observation windows were not complete.
For each eligible post, I counted other articles with the same tag published within a 24-hour period centered on my post’s publication time. I excluded my own articles. The resulting comparison contained 602 neighboring articles. In my published count, 134 had at least one reaction and 60 had at least one comment.
What the numbers say—and what they do not
| Measure | Reported result | What it counts |
|---|---|---|
| Articles with at least one reaction | 134 of 602 (22.3%) | Any article in the comparison sample with one or more reactions |
| Articles with at least one comment | 60 of 602 (10.0%) | Any article in the comparison sample with one or more comments |
The percentages are rounded from the reported counts. They do not mean that the remaining articles received no attention of any kind; they mean only that they did not meet the specific threshold of at least one reaction or comment.
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These are my reported measurements, not independently reproduced platform statistics. The published article does not provide the 602 underlying rows or a calculation artifact, so readers cannot recalculate the pooled results from the material presented.
The comparison was local, not platform-wide
The sample represents articles carrying the same tag and published within a centered 24-hour window around one of my posts. It is not a random sample of DEV Community, and it does not establish how often all DEV articles get reactions.
The rates also varied across the six windows with enough neighbors to report. The two windows with four neighbors apiece are included below because they influenced my method, but they are too small to treat as reliable rates.
| Tag | Neighboring articles | Articles with at least one reaction |
|---|---|---|
| python | 120 | 17.5% |
| webdev | 30 | 20.0% |
| opensource | 89 | 22.5% |
| programming | 179 | 22.9% |
| opensource | 125 | 24.0% |
| discuss | 51 | 27.5% |
| Two additional windows | 4 each | One reaction in each (25.0%); too thin to interpret |
The two opensource entries are separate windows, not a combined estimate. The rates describe those particular neighboring groups, not a ranking of tags or a prediction for a future post.
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Why I set a minimum sample size
Each four-article window had one article with a reaction, which produces a superficially neat 25.0%. But with only four observations, one article changes the rate by 25 percentage points. I treated those windows as “too thin” rather than as evidence that silence was or was not unusual.
I then used 30 neighbors as a minimum for interpreting a window. That was my practical rule, borrowed from a prior GitHub comparison standard; the analysis does not establish that 30 is a statistically validated cutoff. The lesson is narrower: label very small groups plainly instead of giving their percentages more precision than the sample supports.
What a zero-reaction post can tell you
In these measurements, most neighboring articles did not reach the threshold of at least one reaction. So a post with zero reactions is not, by itself, evidence that its writing failed or that its distribution channel does not work. The comparison cannot settle either question.
The neighboring posts may differ in author reach, subject, quality, age, or audience. This is an observational comparison, not an experiment: it does not identify why any post did or did not receive a reaction. Its value is as context for judging one outcome, not as a causal explanation or verdict.
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Keep views and unfinished windows out of the comparison
I did not compare my 103 page views with neighboring posts’ reactions or comments. Views were visible for my own articles, but I did not have a public counterpart for those figures in this comparison. Mixing different measures would not answer whether neighboring posts earned reactions.
I also excluded articles that were younger than half of the centered 24-hour window. Without the future half of the window, their comparison period had not elapsed. In my implementation, a nonzero view count indicated that the counter had updated; when it still read zero, the post’s age mattered. That is how I handled my measurement, not a general guarantee about DEV analytics.
A useful way to read a quiet post
Before treating silence as a verdict, define a comparison group that resembles the post you want to assess: use a consistent publication-time window, match the relevant tag or audience, exclude your own posts if they would distort the comparison, and count the same engagement threshold for every article. Set aside immature windows, report the sample size beside each rate, and mark tiny groups as too thin to interpret.
My phrasing for the idea is: “Before reading a silence as a verdict, measure the base rate of the room.” It is a reminder to seek context, not a statistical rule or a claim that every room behaves the same way.
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