As we continue navigating the turbulent social media landscape of 2025, we embark on another Random Labs experiment—this time analyzing how caption length might impact Instagram post performance.
In our previous experiment, we analyzed the impact of using hashtags versus keywords on this platform (check out this blog to see the results). We continue our statistical adventures by analyzing another component of Instagram captions–length. With growing discussions about the increasing impact of SEO on this platform over the years, this analysis will serve as a great supplement to the overall topic.
To analyze the impact of caption length on Instagram post performance, we first need to manually create a variable representing the size of a post’s caption.
Instagram, through Meta Business Suite, does not include such variables in its exportable data. Instead, this data typically consists of standard social media metrics, such as engagement, reach, and more.
Fortunately, most datasets from each platform almost always include a column listing the caption associated with each post. With a little spreadsheet magic, we can create a new column that counts the character length of the caption and add it into our statistical model.
We will be conducting this experiment with some statistical modeling, specifically a Multiple Linear Regression (MLR) model.
A simple example for this methodology would be: imagine you're trying to guess the price of a house. You know that bigger houses usually cost more, but that's not the only thing that matters. The neighborhood, the number of bedrooms, and how old the house is all play a role too. MLR is like a smart calculator that looks at all these factors together to make a good guess about the house price.
In our example, we are testing to see if the factor regarding caption length will statistically have an impact on content performance (engagement or reach).
In our example today, to add more context to the model, we also want to include a variable that shows whether that specific post contained a hashtag or not. This can give us more insights on whether there’s any specific sweet spots with caption length with or without hashtags.
Here are the conclusions for both of our models:
| Variable of Interest | Adjusted R-Squared | Caption Length Significant |
| Reach | 0.0222 | No |
| Engagement | 0.0515 | No |
Today, Random Labs introduces a new statistical measurement: the Adjusted R-Squared. This metric is frequently used in MLR’s to provide a more accurate assessment of a model's predictive power than a regular R-Squared.
Let’s use another fun example to explain this value:
So, if adding "what the student had for breakfast" doesn't really help predict test scores, the adjusted R-squared would decrease, telling us it's probably not worth including that factor. This is key for our social media example, which innately will include various potential performance factors.
Based on our output in the study, we can see that we outputted a very low Adjusted R-Squared value for both models including caption length as a predictor for a post’s reach or engagement. The low values for both tests suggests that after accounting for all the different variables relating to a post’s caption, including caption length, they only account for 2.2% and 5.1% of post’s reach and engagement respectively.
We can confidently conclude that a post’s reach and engagement are more likely affected by other factors.
These results aren’t particularly surprising to us, as we understand the dynamic nature of social media content. A post’s performance on most platforms is ultimately difficult to predict, although we have identified significant factors in the past. Specifically, when it comes to captions, we are likely looking at the keywords themselves as more of an impactful component as opposed to the overall length of the caption: quality over quantity.
We also continue to emphasize in each of these experiments that all social media profiles are different and that drawing conclusions tailored to your profile’s needs is key.
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