Manipulating Crypto Prices Through Sentiment Shill Armies to Trick the Bots – Trustnodes

Manipulating Crypto Prices Through Sentiment Shill Armies to Trick the Bots


In the zero-sum game of day trading any advantage can be worth millions with the relatively new field of data mining having potential to give you an edge.

The idea is as simple as it is complex. If you know what the crowd is about to do before they do actually do it, then you could be ahead of the herd. Sell first or buy first.

For commoners, crunching the data or even getting hold of it is no easy task. For the money printers and their investment managers, it probably went something like this with what follows being a product of Trustnodes’ imagination.

So we have all these data – someone perhaps said in Google’s 20% free time. Yet we only use playable links to rank. Couldn’t we feed the bots so much information that it eventually knows what a searcher meant based perhaps on probabilities curves?

We can hire many low paid human curators and based on their exercise of judgment we can categorize things so finely and can link such categories through if/thens to the point the bot effectively knows.

Then, instead of people coming to us to go to a link, we can give them the information straight on. Eventually, in fact, the bots may get so good at it that Google no longer has any actual links. Instead, it instantly provides what anyone might want.

At Facebook, probably around the same time, probably the same thing happened with the focus here being a lot more directly on what ad a user wants. Do they want to read more similar news and if so would they like this blog that wants to promote itself? Do they want to buy something?

And then perhaps the unasked question which may have well changed the world in some ways: Are they having a baby? Does that mean they are to buy a lot of baby related stuff?

The question simple, but the paradigm shift complex for they were no longer looking at what we want. Instead they begun trying to predict what we probably will want based on fairly simple ifs and thens.

If he is graduating, he probably is looking for a job, so he probably wants to see this job site we can put in front of him or her. If he is going on holiday, he may well want a suitcase.

Then another tiny but huge paradigm shift. He bought the suitcase last year when he went to Ibiza. He has not bought swimming shorts in a long time however. He probably has three or four pairs based on his purchasing history, but 2 or 3 of them should now be worn out to the point he needs some new shorts, but hasn’t thought of it yet. Let’s nudge him. Put up an ad of some nice shorts, lets see if he clicks.

The picture here now complete for what has been achieved, although to a crude extent, is the ability of bots to know what we want, what we will want, and to even tell us of what we probably want, but haven’t quite thought of it yet.

Obviously, as the above was developing, it was being developed. The code does still remain very dum, but the coders are getting smarter and smarter through an incremental process of putting bricks upon bricks.

The coders, so being human, begun talking about much of the above at first in a: hey look at this cool new method that can give us these new abilities.

So data mining, machine learning, artificial intelligence, or whatever you might want to call it, got out of Google/Facebook and into other areas.

The most prominent such area being politics. The story here probably begins in Britain 2010 when a newly elected David Cameron brought to the halls of power a Silicon Valley techie, Steve Hilton.

They publicly spoke back then – through the whispers of paper columns –  of what you might call a carrots policy. They called it a nudging approach towards incentivizing compliance or good behavior like exercising. The idea being that instead of punishing with fines and so on, you might perhaps first send a letter where it’s a small thing. Or you might pay them to exercise.

David Cameron won a majority in the first time for a conservative leader since the 80s. Probably did so for many reasons, with the story perhaps not quite starting here but in bitcoin 2015.

The blocksize wars had two teams of very able coders in a neck and neck political campaigning of such intensity and cleverness that probably rivals any real election.

Shill armies, vote manipulation, bot comments, censorship, slogans and even hats were used to influence the watching public in one way or another.

“This cool new method that can give us these new abilities” was becoming a tool of manipulation used now no longer just to buy ads, but to form opinion.

Coders now no longer thought this new tool was a cool thing. They begun whispering, mainly on Hacker News, of the best of a generation spending their time to influence grandma to click ads.

Tools that were in full force during election 2016. An election that pitted the internet “media” against the TV media, an internet media that on r/politics descended into censorship.

For two-three months, all social media, whether crypto-twitter or scientists-twitter, descended effectively into a chorus amplified by bots on both sides.

So bringing us to the topic of potential price manipulation through sentiment data. Before these tools, developed in Silicon Valley (primarily Google) with probably the best intentions, made their way to politics, they first spread to finance.

High frequency bots was and remains a new area in trading where if and then code commands sub-second actions. Code that can be made quite complex through data-mining.

What is currently publicly available through things like Santiment is the first stage: what do they want? Here, it is quite simple. You just collect what they’re saying and you basically “read” it through some sort of filter.

Crypto sentiment analysis, January 22 2019.

Before this tool got out to the wider public, it is probable they were used to inform trading decisions perhaps even by the bot automatically. Something like if chatter is beginning to rise by x% about x, then buy x amounts of it.

There may still be plenty who use this simple form, but in an informations arms race it is probable the real bots have gotten a lot more sophisticated. As may also be the case for those who code them.

It is rational to suspect that a lot of comments on say Ethtrader daily are “fake.” It may well be “fake” humans or fake bots or fake voting, but the chorus aspect of it does indicate there is carroting going on.

Comment after comment effectively saying the same thing but in different words, sometimes emotionally hard-hittingly so, may well be an attempt to nudge you towards what “they” would like you to think.

Thus the attempt may be less at trying to influence the bots, and more in trying to influence the humans through bots.

Then those who control the bots so “control” the humans and since the bot controller is the one to tell the bots what to do, then naturally the bot controller gets to act before the herd.

All of the above is stated as a product of the imagination. There is no evidence worthy of proving any of it when reason does fine in exercising judgment in the absence of hard evidence.

So now going to the future in this imaginary world you’d think smart men would leak these tools and open them to all. Then manipulation can be counteracted just like speech is, by amplifying truth or reason.

That means any child born today must by necessity learn if and then together with abc. Not in high school, but first grade. Because knowing how to code may well become just as vital as knowing how to read.

It also means that we have to overturn the Securities Act 1933 because for freedom to live there must be a business model for open source code which is available to all. Code like how to run a bot army.

These tools in the hands of the few could enslave us, but in the hands of the many, they could well liberate us.



Leave a Reply

Your email address will not be published.

You may use these HTML tags and attributes: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <s> <strike> <strong>