YouTube comment benchmarks for tech channels

Tech and science viewers often use comments as a second search box: they ask whether something works, what to buy, and how a setup differs from another one. This profile shows how those questions and conversations appear in the 2026 study.

2026 category profile · Tech & Science

Are tech YouTube comments mostly questions or purchase research?

The tech profile is the most question-heavy of the eight content groups. Its question mix also has an unusually visible product, price, or purchase layer. That combination changes the best workflow: a creator can treat a comment as both a support request and a buying signal, while still checking the exact product, version, and use case before replying.

A useful answer needs more than a generic thank-you. Separate setup problems, comparison questions, and buying questions into different reply paths. A short answer that names the relevant model or timestamp is often more useful than sending every question through one template.

At a glance

Tech & Science YouTube comment statistics

These are the measures most useful for deciding what to answer, what to moderate, and what to investigate next. The details under each card state the denominator.

1,296

Channels in this profile

Broad-sample channels assigned to this content group

17.6%

Questions

Share of weighted sampled comments classified as real questions

11.6%

Creator reply rate

Matured sampled comments with a visible creator reply

79.6%

Questions unanswered

Matured real questions with no observed creator reply

4.4%

Hostile comments

Share of weighted sampled comments with a hostile stance label

15.5 words

Average comment length

Average words per sampled comment in the profile

70%

English comments

Comments assigned English by the study's language classifier

2.12%

Buying or business

Share of weighted sampled comments with a commercial signal

Category comparison sample: 82,111 sampled comments. Rates are rounded and remain separate from the study's creator-business sample.

What viewers are doing

The shape of a Tech & Science comment section

A category benchmark becomes useful when it names the work hidden inside the word “engagement.” These distributions show what the sampled comments were doing and how the questions were framed.

Distribution

What comments are doing

Primary function of each sampled comment

  1. Thanks or reaction26.4%
  2. Discussion or debate21.1%
  3. Asking for information15.2%
  4. Other9.7%
  5. Feedback or correction9.7%
  6. Personal story or result5.6%
  7. Request for the creator4.2%
  8. Insult or attack3.4%
  9. Spam or self-promotion2.1%
  10. Buying or business request2.1%
  11. Talking with other viewers0.4%
MeasureShare
Thanks or reaction26.4%
Discussion or debate21.1%
Asking for information15.2%
Other9.7%
Feedback or correction9.7%
Personal story or result5.6%
Request for the creator4.2%
Insult or attack3.4%
Spam or self-promotion2.1%
Buying or business request2.1%
Talking with other viewers0.4%

Denominator: sampled comments represented by the Tech & Science category row (82,111); shares are weighted within channels.

Function labels are model-coded. Shares add to approximately 100% after rounding; one primary function is used for each comment.

Distribution

What real questions ask

Question type among the profile's real questions

  1. How-to or troubleshooting23.8%
  2. Other21.1%
  3. Product, price, or purchase15.6%
  4. Gear or source12.8%
  5. Clarification10.5%
  6. Opinion or discussion8.8%
  7. Topic request3.9%
  8. Personal or creator3.4%
MeasureShare
How-to or troubleshooting23.8%
Other21.1%
Product, price, or purchase15.6%
Gear or source12.8%
Clarification10.5%
Opinion or discussion8.8%
Topic request3.9%
Personal or creator3.4%

Denominator: real questions only. This is not the share of all comments.

Question types are model-coded and describe the information sought by question askers.

Distribution

What creator replies look like

Style among creator replies the study could read

  1. Substantive44.9%
  2. Brief acknowledgment27.4%
  3. Generic template23.1%
  4. Other3.9%
  5. Defensive or hostile0.7%
MeasureShare
Substantive44.9%
Brief acknowledgment27.4%
Generic template23.1%
Other3.9%
Defensive or hostile0.7%

Denominator: creator replies that arrived and could be classified.

Reply style is a composition of observed replies; it does not show how many comments received a reply.

Use the benchmark carefully

What this pattern means for your workflow

Commercial share is the share of sampled comments classified as a buying or business request. The question mix is separate: it describes the kinds of real questions, including product and purchase questions, and its denominator is questions only. The two percentages answer different questions.

01

Split support from buying questions

A troubleshooting request needs a precise fix; a purchase question needs current, disclosed information. Give each one its own review rule and response template.

02

Answer comparisons with a reason

When viewers ask which device, tool, or setup to choose, explain the deciding constraint. A useful comparison earns more trust than a bare recommendation.

03

Escalate version-sensitive claims

Software releases, prices, and compatibility change. Hold any automated answer that depends on current facts for a human check before it posts.

Compare the eight groups

Where Tech & Science sits in the category set

The comparison is here to prevent a single category number from becoming a universal goal. Select another group to read its own profile and interpretation.

Content groupQuestionsHostileCreator replyUnanswered questions
Education & How-To13.5%4.2%16.2%77.3%
Tech & Science17.6%4.4%11.6%79.6%
Gaming12.1%4.5%10.2%82.5%
Lifestyle & Interests10.4%5%14%75.9%
People & Vlogs9.4%5.4%7.7%86.4%
Entertainment & Comedy8.3%6.1%8.1%83.3%
News & Commentary7.1%18%4.5%89.7%
Music6.9%3.4%9%83.8%

Source: broad channel sample and recent comments. This table keeps the eight category rows together; it does not create unsupported category-by-subscriber-size estimates.

Make it personal

Compare the benchmark with your own channel

Category averages are context. A free CommentShark audit shows the unanswered questions, high-intent conversations, and audience tone on a real public channel. No signup is needed to start.

Read the denominator

How this Tech & Science benchmark was built

What the profile covers

This row comes from the broad channel sample in The YouTube Comment Report 2026. The study collected public comments from channels found through search, screened the channels as mostly English, and assigned each channel to the most common YouTube category in its recent uploads.

The profile includes 1,296 channels and 82,111 sampled comments in the category comparison. The comment count is a sample base, not the number of comments ever posted on those channels.

How to read each rate

Question, stance, language, comment function, and reply style are model-coded estimates. Reply rate and unanswered-question rate use comments and questions that were at least seven days old, from channels with enough captured history. Reply style describes the replies that arrived, so it is not another reply-rate measure.

Numbers are rounded. A category result describes this study's sample and cannot be treated as a census of YouTube or as a cause of channel growth.

Source: the immutable study snapshot collected August 19–22, 2026. Read the full methodology and limitations.

Questions creators ask

Tech & Science YouTube comment benchmark FAQ

What is the tech YouTube question rate in the study?

The tech and science profile's question rate is shown in the benchmark cards above. It is the share of sampled comments classified as real questions, after weighting the comment sample within channels.

Do tech YouTube comments show buying intent?

Yes. Buying or business requests are a larger share of the tech profile than in several other categories, and product, price, or purchase questions are a distinct part of its question mix.

Does a tech comment benchmark predict sales?

No. A buying-intent comment is a conversation signal, not a sale or a forecast. The study measures public comments and creator replies; it does not connect them to a channel's revenue.

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The parent YouTube Comment Report includes the full dataset context, study chapters, citation, and limitations.