Christopher Penn discusses the importance of testing assumptions in social media algorithms, using machine learning and data science tools. To understand the impact of hashtags on reach and engagement, for example, one would download all of their Twitter data and run a statistical analysis to determine if there is a significant effect. Penn emphasizes the importance of testing assumptions using a large and well-balanced dataset, ideally one’s own or one’s competitor’s data, to draw accurate conclusions. Testing is key to understanding the true impact of different social media strategies, and ultimately, improving marketing performance.
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What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.
Christopher Penn 0:00
Jen asks a follow up question from a previous episode of the Trust Insights livestream on social media algorithms.
She asks, How do we know that the conclusions? For example, hashtags do or don’t matter for retweeting or for visibility? How do we know that that’s a true statement? The answer is, you test, you build models to test those assumptions.
And the way to do that is using machine learning using some data science tools to prepare the data.
And then machine learning to build models that are statistically valid, statistically accurate.
To understand the outcome, I’ll walk you through this pretty briefly, let’s say we want to test to see if using too many hashtags dampens our reach on Twitter, how would we know? Well, to start, you would download all of your Twitter data, right? You would maybe spend a week, a couple of weeks doing tweeting with extra hashtags, maybe instead of one hashtag, you put in three, or four or five, you vary it up, but it’s more than what you usually do.
And then at the conclusion of that period, you would take all of your existing data, maybe six months worth of data.
And using either causal inference or causal inference with Bayesian static time series modeling, or propensity score modeling, you would essentially say, Here’s my treatment period, this is the period of time when I applied this treatment, I used double the number of hashtags to triple the number of hashtags.
And here’s my control period of the past six months worth of data, these software packages will say great, let’s take a look at everything else that is in this dataset.
And then look at our treatment period d noise any variations or any variability that that is statistically present in both sets, you know, sort of the things you would have happened anyway, business as usual.
And then from the remainder say, Okay, this is the, the impact of the treatment of the change that you made.
These techniques, these these datasets, techniques are very common, they are very common, particularly in things like biostatistics, where you’re taking an actual medication, and you’re applying it to some people and you’re applying a placebo to other people.
In doing so, because you can’t AB test a person might you can’t split that person in half and give half the person one treatment and half the person not.
You try to find people who are similar to that person in age, physical condition, maybe ethnicity, things like that, you try to control for as many variables pot as possible.
And you have control group and you have a test group, the exact same thing applies when you’re testing out a social media algorithm, maybe you and five or six other social media managers all team up and you run a test together.
Or maybe you download very large datasets from bunches of different accounts, if it’s on a network where you can do so like Tiktok, were Twitter.
And you run those models.
And you see, is that statistically valid? is the number of hashtags does that change? The reach or the engagements of these different accounts? That’s how you draw those conclusions.
When I ran those tests to see if the number of hashtags impacts the level of engagements, which is a proxy for reach, because no one can engage if they don’t reach them? The answer was, it had almost no effect it was there was no statistically relevant effect over it was three and a half million tweets.
So as certainly a large enough sample size to look at people who used two or fewer hashtags, or people who use more than two hashtags in a in a treatment, and it didn’t really matter, there was no difference.
The same thing was true for other things like time of day and day of week.
So to answer John’s question, you have to test your assumptions using the data that you have available to ideally a lot of it, ideally, data to well balanced, that is that has the ability to remove noise or remove effects in the data that could be causing accuracy issues, remove covariance, for example, and correlates.
But in doing so, you can ask the questions of the data that you want to know.
And what’s critically important is you want to run this ideally on your data, or your data, MAE and maybe your competitors data, because as we know, for particularly things like social networks, where there are these graph networks, and they’re things can happen and phenomena that can happen locally within part of the network.
That is not true for the network as a whole.
You want to run this with your data to see if there’s something that is true about you and your peers and your competitors.
That might not be true about a Kardashian as an example.
Those people and and people outside your industry, maybe they’re going to have different types of interactions and their portion of the network.
So build your models, test your assumptions, look for those statistical outcomes that tell you that something does or does not matter, and then apply the appropriate strategies.
Thanks for asking.
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