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Views and comments measure different things: a view records a page visit, while a comment requires a reader to choose a public, higher-effort response. A quiet comment section does not by itself mean a technical post failed. Readers may have found the answer they needed, prefer not to post, or discuss the article somewhere else. The evidence available offers useful possibilities to investigate, but no reliable comment-rate benchmark or single fix for technical blogs.

Why do people read a technical blog but not comment?

They may get what they came for

A reader who arrives from search, finds a command or explanation, and leaves may have had a successful visit. Reading is useful without being social. A study of four moderated digital health networks observed passive readers as well as contributors, but those communities do not establish how readers behave on technical blogs.

Posting takes more effort and exposure than reading

A reader might hesitate to ask a basic question, correct an explanation in public, or disclose an opinion. In a 2016 US survey about online news comments, respondents who commented cited argumentative comments (33.9%), avoiding conflict (32.9%), uncivil commenters (29.4%), not knowing enough about the topic (28.2%), and not wanting to share an opinion (27.7%) as reasons to avoid commenting. These are self-reported barriers in news-comment spaces, not measurements of technical-blog audiences; they are prompts to examine your own comment form, tone, and moderation.

The response may happen somewhere else

A post can be discussed in a workplace chat, issue tracker, forum, or social network rather than in its on-page comments. In the same 2016 survey, 77.9% of online commenters said they had commented via social media. That does not show where a particular blog’s readers respond, but it is a reason not to treat the comment count as a complete record of discussion.

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The question may be too vague

“What do you think?” leaves readers to invent a useful response. If feedback would help, ask a specific, answerable question tied to the article: “Which runtime version are you using?”, “Does this fail on ARM64?”, or “What would you change in this migration plan?” This is practical editorial guidance, not a proven intervention from the studies cited here. Ask because the answer could improve the discussion or the article, not simply to inflate the total.

Is 90-9-1 a useful target for blog comments?

No universal technical-blog comment target is established by the evidence here. The 90-9-1 rule is a shorthand about unequal participation, not a dependable rate to expect from your readership.

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A 2014 observational study by van Mierlo and colleagues examined four moderated digital health social networks: among 63,990 actors and 578,349 posts, fewer than 25% of actors made at least one post. Across the networks, lurkers produced 1.3% of content, contributors 24.0%, and superusers 74.7%. Those figures illustrate how participation can be concentrated, but they describe health communities—not technical-blog readers, or the share of blog visitors who comment. The study itself notes, “To date, very little empirical research has been conducted to verify the 1% rule.”

Other historical figures should be read just as narrowly. A 2006 Pew Internet & American Life Project survey reported that 87% of surveyed bloggers allowed comments and 82% had commented on someone else’s blog. These are findings about bloggers at that time, not current adoption or a benchmark for today’s technical sites.

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Are blog comments still important?

Comments can surface corrections, reproducible bugs, follow-up questions, and useful reader experiences. But raw volume is not the same as reader value. The visible commenters may be a small, highly engaged slice of a much larger audience: a 2005 study of bloggers’ expectations of privacy and accountability describes writers using comments, access logs, and referrer links to learn about audiences, while cautioning that most readers may remain unknown.

Judge comments alongside other outcomes that matter to the post. Returning visits, newsletter signups, clicks to code or documentation, downloads, and direct questions each reveal something different; none should be treated as interchangeable with comments or proof of success on its own.

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How to diagnose a views-and-comments gap

  1. Make sure a reader can comment. Check that comments are enabled on the specific post, the link or form is visible, and the process does not impose avoidable account or identity hurdles.
  2. Check where visitors come from. Search visitors arriving at a reference answer may want a quick fix; a social share or community discussion may bring people with a different reason to visit. A page view alone does not reveal intent.
  3. Compare relevant outcomes. Review comments alongside returning readers and useful actions your site can measure, such as newsletter signups, documentation clicks, downloads, or direct questions. Interpret each metric separately.
  4. Review the tone and safety of the space. Explain how comments are moderated and make clear that respectful corrections and beginner questions are welcome. Remove abusive content consistently rather than manufacturing controversy to draw replies.
  5. Ask readers directly. A short survey, an invitation to reply by email, or a question in the community where readers already gather can help distinguish a preference for silent reading from a preference for another channel or format.
  6. Change one thing at a time. If the form is hard to find, improve its visibility; if the invitation is vague, try one specific question. Compare useful responses as well as raw counts. The evidence cited here does not identify an intervention that will work for every technical blog.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Support on Ko-Fi

Should you encourage on-site comments or another response channel?

Choose based on your readers and the kind of feedback you need, not a generic goal of making the comment count larger. Compare the options on these practical dimensions:

  • Reader effort: How many steps or disclosures does each channel require?
  • Audience fit: Where do your actual readers already discuss technical work?
  • Response usefulness: Is the channel likely to surface reproducible bugs, questions, or corrections—or mainly quick reactions?
  • Moderation and safety: Can you maintain respectful discussion and avoid exposing readers to unwanted risk?
  • Measurement: Can you tell whether the channel helps readers, rather than merely increasing a count?

These are decision criteria, not experimentally validated rankings. The available studies concern digital health communities, news commenting, and bloggers—not technical-blog comment rates. Treat their findings as hypotheses to test against your own interface and audience.

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