You Ask, I Answer_ How To Identify Fake Influencers

Grace asks, “There’s been a lot of news about people who fake being an influencer by buying followers and colluding with groups of influencers. How do you identify fake influencers?”

Great question.¬† Let’s first discuss what brands actually want, even if they can’t measure it effectively.

  • Awareness
  • Engagement
  • Action

Second, let’s identify the ways someone can fake influence.

  • Share bots
  • Paid followings
  • Pods are a gray area

Which fake tactics are the worst? Paid followings – because they’re generally bots, and bot followers won’t do anything to increase actual awareness. Share bots aren’t as terrible, because there’s a non-zero chance they share a piece of content with actual humans. We’ve all read how effective bots were in influencing politics. They couldn’t have done that if they were sharing only to other bots. Pods – even though pods are considered a black hat tactic in influencer marketing, the reality is that they work and they don’t damage a brand’s goals.

Given the above, how do we identify bad actors? Combining a few metrics helps us identify warning flags to examine likely problems.

  • ¬†Groups that self-reinforce in a network graph
  • Entities that broadcast but never have anyone talking about them
  • Entities that have distorted metrics (thousands of likes, not a single comment, etc.)

Watch the video for full details and an example in fashion influencers.

Can’t see anything? Watch it on YouTube here.

Listen to the audio here:

Download the MP3 audio here.

Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.

In today’s episode, Grace asks, there’s been a lot of news about people who fake being an influencer by buying followers and colluding with groups of influencers. How do you identify fake influencers? Great question. Generally speaking, people behave in certain ways. And when you see accounts that have data that skews pretty far from that, that’s a way to identify likely problems. So let’s first discuss what brands actually want in social media, when they’re working with influencers, even if they’re not particularly good at measuring it, there’s three general pockets, right. So a brand will want awareness and alternatively, or in addition to awareness or affinity to consumers like that brand. They want engagement people talking to the brand liking, commenting on stuff like that sharing and then want action they want stuff that leads to business results can be website traffic, it could be purchase right on the social network. But you know, Instagram offers shopping right on board, it could be

filling out a lead form, it could be showing up at a retail store, it could be promoting being an evangelist for that ran into and and wearing their logo and stuff on on your apparel. And so those are some of the ways that that’s something that brands are looking for. So

with that, what are the ways that you can fake influence? Well, there’s there’s three or four different ways with bots fake accounts, robotic accounts, there are there are share bots which will automatically share anything that you know, they pick up content and reshare it over and over again, there are paid followings. We can buy followers. And there are these things called pods, which are kind of a gray area pods are private groups of people who collude behind the scenes to promote something in a Facebook group in Slack channel, whatever, you just see the effects of it, but you don’t necessarily see the conversations and agreeing Hey, we’re gonna prove it promotes Susan stuff this week. So Susan is going to be the rock star that you can next week, George is your turn. Now, which of these tactics are the worst? From a brand perspective, generally speaking, paid followings are the worst because their butts and bought followers don’t do anything to increase actual awareness or actual trust or affinity and a brand.

If someone buys 30,000 followers, they had 1000 the start with Yes, they may be seeing, oh, you know, you may be sharing to an audience of 30,000 on paper, but you really like sharing to an audience 1000 people share bots aren’t as bad because there’s a nonzero chance that they share a piece of content with actual human

we saw this we read about it’s been in the news about how effective

bots were in influencing politics and influencing elections. I know they could not have done that, that if they were sharing only two other bots, they were able to engage with actual people promote a message those people want to promote and

get a good result.

And then on the third one is pods. Even though pods are considered a black hat tactic by a lot of folks in social media, the reality is they work and they don’t damage of brands goals. Right. If a brand’s goals are awareness, engagement and action, get 500 parents sharing a link.

That’s not a bad thing. That is

the belief that pods are bad assumes that you only care about individual influencers, if you can get an entire group of moderately influential people to do something Mission accomplished. So

given all that above, how do you identify bad actors? The answer, unsurprisingly, is analytics. What we’re looking for in our data are anomalies, right. So we want to identify groups that self reinforce entities that bro broadcast a lot, but never had anyone talking about them. And entities that have distorted metrics, where there’s like, you know, millions of one metric and zero of something else. So what we’ve got on screen here, this is from a piece of software called, and it requires social media monitoring data. So I have separate software that pulls in the data cleans, it prepares it to be useful in network graphics software. But let’s take a look at some of the metrics in here as a way to identify in fake influences. If we look at 25,000 conversations or so about fashion, you can see there’s really no no easy way to understand what you’re looking at. But what we’re looking for is this measure here, this, this, it’s called eigenvector sensuality. And what it is, is a measure of how many people talk about you versus how many people you talk about, a lot of these networks do a lot of broadcasting, but not a lot of receiving because they have no influence. They particularly on fake follower funds. So anything that has just no connections on that graph at all. And none of this, the sensuality measure is clearly not something that’s going to be influential. And as we as we go down the list here, you can see the influence of still zero, even though there are some accounts to have in this case, like, yeah, this one’s got broadcasting 49 different conversations. But no, but he’s talking about in the the influence measure, still zero. So all that was going to get rid of all those people in the network graph. And then there’s some accounts get post and re grant and stuff that clearly also don’t add anything to the conversation. So we’ll go ahead and get rid of that. And now we rerun this. And this is a process you do a few times to clean and refine the network graph until you have something that is usable, going through

this process.

Going through Now, a few iterations, we’ve gotten rid of everything that is not influential in the sense of contributing to the graph and look how much neater and clean to this graph is. Now let’s go ahead and actually put some names and clustering on this. And if we look carefully, now, we see this is super tight, evenly sized clusters, those are pods now, whether they are pods that are human or bought doesn’t matter that we know we can identify through the fact that they have these self referential loops within each other, that they are almost certainly automated compound that with the fact that these are probably not necessarily folks that you have heard of, and that they share all share similar names. And we’ve successfully identified that these are clusters that we will probably want to discount or filter out in our monitoring software. So that’s what one of the things that comes next is in the process. Once you identify the bad actors, you don’t just delete them and and go at the rescue, then feed that back into your social media monitoring system train and teach it like these accounts to not pay attention to and over time what will happen is you’ll get a much cleaner view of the landscape because you’ll be able to remove that stuff from your all your monitoring, once you’ve done a few passes of that you then start to see more natural networks of things that appear actually like networks rather than just a couple of big self referential clusters. And that’s when you know you’ve arrived at an influencer list that is meaningful that is going to get you reach into a community which is the whole point to get that awareness to get that engagement and to get that action so that we would be able to say like this this person here who is highly interactive within this particular Instagram community is the influencer so if this is a group that we care about, then that’s going to get us the the juice that we were looking for. This is not easy stuff this is all machine learning and network graphing and statistics and stuff so and and to do it for a really large space would require some decently heavy compute time so that’s one of the reasons why you don’t see more of this in the influencer marketing space and why companies even companies that are reputable otherwise it’s social media monitoring why they’re in influencer identification algorithm. So so bad because this stuff requires really heavy iron in order to to get you the answers that you want about who’s actually influential. So your best bet as a if you’re running influencer, identification for a brand for client is to have a narrower context and dig into that very specific context. Rather than trying to go after a huge topic. Like all fashion, you’re going to need super computer power to tackle all fashion or all food. If there’s a specific thing like I want the top influencer about Gouda cheese, you’re probably going to do a little bit better. So great question. Grace of a complicated question. And as you can see, require some heavy lifting in order to be able to identify things in the data and, and clean your data properly to get to the insights you’re looking for.

But this is how you do it. And it’s now up to vendors and and providers and stuff to be able to refine that and turn that into a product that doesn’t require a data scientist to do gap. And of course, if if there’s something you need to have done, get a data scientist to do it for you.

Because again, a lot of what’s on the market right now the software isn’t up to doing this sort of heavy back end research. So as always, please subscribe to the YouTube channel and to the newsletter and I’ll talk to you soon.

Take care if you want help with your company’s data

and analytics visit Trust Insights calm today and let us know how we can help you


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