Celebrity followers are bots?

I keep hearing that large accounts or celebrity accounts have a lot of bots as their followers. I don’t see how this makes sense because why would any body make their bot follow a celebrity account? Furthermore when going through celebrity followers most don’t seem like bots to me.

My question is, how do I identify these fake users and target so that I can avoid them.

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By targeting the active users: users who commented, who liked, followed recently, etc.

would you be using a specific software for this? Or manual work?

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Thanks. Software

you will NEVER eliminate them. with that said, choose your filters properly( bio pic, profile…etc)
avoid the super big ones, choose smaller same celebrity pages. there must be thousands of kim kardasion pages…use them.

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Warmup phase . You follow suggested users .

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Bots usually have domains in bio… just saying.

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Last time i checked my followers are 95% real ones!

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Oh, that makes sense.

Haha gave me a good laugh

A good filter technique which I saw it wasn’t specified by other users here, is to add blacklisted words into the bot filter, so that will not follow accounts containing (in either username or bio) spammy words like crack, hack, giveaway, free, and so on. This will filter bots out by a lot.

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Filters are dope

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@thetraveller, do you have a good list you can share?

My keyword filters for Follows bios, or in captions when liking/commenting:

free,like,get,giveaway,follow,iphone,win,.com,.net,.info,.org,buy,purchase,get,sale,download,bio,link,ebook,gift,gifts,buy,iphone,DM,enquires,inquiries,promotion,promote,shipping,win,tickets,event,events,.com,bit.ly*,.net,.org,info,http*,store,shop,app,*%,limited time,

Edit: The ones in italics have an asterik * either side (but have become italic). That’s a wildcard, in case they are part of URLs or extended words.

Also, sometimes the filters for reposts include months or years, as they might be promoting an event/sale/image based on a date, and that helps filter out unnecessary sales products, or outdated images.

@jpfletcher8

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I guess you can try to filter them with all the methods above but is not fool proof.

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