Estimation: Displaying Estimated Reading Time and Word Count

Estimation: For Reading Time:

Liquide Code -

{% assign reading_time = article.content | strip_html | split: ' ' | size | times: 1.0 | divided_by: 200 | ceil %}{{ reading_time }} min read

Here’s breakdown the code

assign reading_time = article.content: This creates a variable named reading_time and assigns the full body text of blog post. 

| strip_html: This filter removes all HTML tags (like <p>, <strong> or <div> etc) so that we get only the actual words that are counted.

| split: ' ': This turns the long string of text into a list (array) by breaking it apart every time there is a space. Note that, if you use ‘’ (no space) it will return character count.

| size: This counts the total number of items in that list, which effectively gives you the total word count of the article.

| times: 1.0: This converts the whole number into a decimal (float). This is necessary to ensure the division in the next step is precise and doesn't just round down immediately.

| divided_by: 200: This divides the total word count by 200 (we assume the average words read per minute, actually 200 words per minute is an industry standard).

| ceil: It rounds the result up to the nearest integer (e.g., 2.1 minutes becomes a "3 min read").

{{ reading_time }} min read: This outputs the final calculated number onto the page followed by the text "min read."

For Word Count: 

{% assign word_count = article.content | strip_html | split: ' ' | size %}{{ word_count }} words

Since the word count logic follows the same steps explained above, we won’t repeat the breakdown here.

The Estimation of reading time and word counts using Liquid code is a highly efficient, native solution to boost blog user engagement without relying on heavy third-party apps.