Are likes, shares, or comments more important for TikTok video success?


Summary

TikTok’s recommendation algorithm is an iterative process which repeatedly displays videos to larger and larger test audiences, until engagement from the test audience falls below some threshold. It is unknown how “engagement” is measured, but it is usually speculated to be some combination of likes, comments, and shares. Various people have hypothesized the weights of these metrics (e.g. one share = 10 likes), but, to our knowledge, no one has ever measured it. We therefore attempted to measure this on a representative sample of ~20,000 videos.

Our research concludes that these metrics are highly collinear and no predictive value is found from comment and share count. (I.e. you should just optimize for likes, and don’t need to optimize for shares or comments.) You will need about 1/5 of your viewers to like your video in order for it to be promoted.

Background and Motivation

More background on the structure of the TikTok algorithm can be found here. Veed contains one of the more technical explanations of the TikTok algorithm. They hypothesize the following:

Now that TikTok knows some basics about the video, it will now boost the video to a small number of users. After booting [sic] the video, an evaluation will happen based on how the sample set of user interaction with the piece of content. Each metric that is tracked has an associated score that varies in value. Here is an estimated score hierarchy that awards content per user interaction.

Rewatch rate = 10 Points
Completion rate = 8 Points
Shares = 6 Points
Comments = 4 Points
Likes = 2 Points

As you can see from the above, per-user engagement rates are the top-rated metrics juxtaposed to likes and comment being the least.

Our understanding is that this score hierarchy was based on the author’s intuitions, as opposed to any evidence, and we are not aware of any other research into possible score hierarchies. Nonetheless, many creators include comment or share-based call to actions (e.g. “share with your tallest friend”). Such CTA’s are commonly recommended for other platforms, e.g. YouTube.

It’s therefore important for creators to understand which metrics influence recommendations, so that they can incorporate CTAs appropriately.

Methodology

A representative sample of ~20,000 videos was used and partitioned into ~12,000 videos from 2020 and ~5,000 videos from 2019. Each video was tagged with the number of likes, views, shares, and comments it received. LASSO, Ridge, and ordinary least squares regressions were run against each data set, with both linear and log-transformed values. LASSO and Ridge hyperparameters were selected via fivefold cross validation.

Results

Year
Transformation
Regression Type
Shares Coefficient
Comments Coefficient
Likes Coefficient
Intercept
Alpha
R^2
2020
None
Lasso
0
0
4.556428
10416.2
3.72E+07
0.798259
2020
None
Ridge
25.16904
-0.26108
4.410611
9774.501
1.00E+08
0.806881
2020
None
OLS
27.0471
-0.25979
4.396494
9752.731
N/A
0.838235
2020
Logarithm
Lasso
-0.03346
-0.05088
1.078338
1.641511
0.012661
0.945464
2020
Logarithm
Ridge
-0.04422
-0.07564
1.099521
1.606834
1
0.945643
2020
Logarithm
OLS
-0.04424
-0.07572
1.099592
1.606708
N/A
0.948446
2019
None
Lasso
0
0
6.705435
3381.721
1.10E+06
0.837942
2019
None
Ridge
-2.62557
8.774306
6.710484
3340.445
1.00E+08
0.838817
2019
None
OLS
-6.47772
52.20791
6.501989
3196.172
N/A
0.862279
2019
Logarithm
Lasso
0
0
1.037677
2.036934
0.021131
0.889898
2019
Logarithm
Ridge
0.013935
-0.01206
1.039334
2.031968
5
0.890604
2019
Logarithm
OLS
0.013507
-0.01301
1.040213
2.030331
N/A
0.901423

2020 Regular

The following graphs compare the actual number of views a video got to the number of views the labeled regression predicted them to receive.

2020 Logarithm

2019 Regular

2019 Logarithm


Discussion

The fit is reasonably good, as demonstrated both by the formal R2 near 0.9 and eyeballing the plots.

LASSO regression drops share and comment count variables from the untransformed data, except for the log transformed 2020 data, where they are assigned small values. There are several reasons to think that comment and share count adds little predictive value:
  1. First, simply based on priors, we might assume that the independent variables are all co-linear
  2. Testing confirms that this is, indeed, the case
  3. OLS results seem rather implausible (e.g. one comment = -17 likes)
  4. Dropping these variables causes only minor degradations to predictive fit

Of course, simply because these variables are not predictive within our sample, that may not mean that they have no predictive value. It could be that so few creators use comment or share-based CTA’s that comment, share, and like counts are heavily correlated, but we would see different results if more creators use these CTAs. A larger sample size may fix this.

Conclusion and Recommendations

It seems likely on both priors and evidence that engagement metrics are correlated, and that optimizing for one over the others is unwarranted.

Creators should simply try to create content people like – comments and shares will follow naturally.

This research done in collaboration with @lilweehag. The code underlying this post can be found here.

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