[D] Is there a way we can score "popularity" on social media posts?

Submitted by Seankala t3_yj2r0y in MachineLearning

I'm currently in charge of a project that deals with social media texts in the e-commerce domain. My objective is that I want to create "popularity scores" for particular items per each week. I'm currently using a very crude metric of using a weighted mean between the number of views and comments per post. However, I'd like to know if there's another way that I can score popularity.

I've currently found papers like this: https://dl.acm.org/doi/abs/10.1145/2661714.2661722 but they're rather old. Wondering if there's a better way to go about this.

Thank you!

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MustachedSpud t1_iun145l wrote

Those sound sensible to me. Only other data that I can think of using would be engagement rate, like how many comments/likes per view.

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trendymoniker t1_iunwsjv wrote

It all comes down to what you’re optimizing for. I’d you have a system want to optimize for likes, count likes. If you want to optimize for shares or watch time count those. If you’re interested in some sort of omnibus “popularity” count whatever weighted sum of those makes sense

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Spirited_Expert64 t1_iuoyfts wrote

Number of views seems reasonable. But the number of comments per posts may be misleading. What if most of the comments are negative? Ideally you would want to do some analysis of the tone of the comments as well.

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Seankala OP t1_iuozuvw wrote

Replying to Spirited_Expert64 (#421,878)

Ah thanks for the comment but I think we won't have to account for sentiment information. I should have probably said "publicity" or something rather than "popularity" (it makes sense in my native language). Negative sentiment would also mean that something is trending, and that's what we're trying to measure rather than how positively people would view something.

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Seankala OP t1_iup00ot wrote

Replying to trendymoniker (#419,923)

Thanks for the comment. I should probably have been a little more descriptive, but we're not really trying to optimize the metrics themselves. This is just one part that's going to be used for a bigger pipeline.

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prohitman t1_iuq9tt6 wrote

A weighted mean of the number of views and number of comments might end up being just fine for your purposes, but sure, it's definitely a little bit crude. Have you thought about adding a normalization step before averaging the two? Since you're dealing with engagement metrics, I bet your dataset is going to be heavily right-skewed anyway, so applying a power transform followed by a min-max normalization would help with that aspect as well.

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LurkAroundLurkAround t1_ivliqj0 wrote

Can you look at long views? They are generally much more informative. Or view more comments interactions

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