Tag Archives: click farms

Let’s dance

That is the setting. Several papers gave it, but I am going to stick to the Guardian for a specific reason. The article (at https://www.theguardian.com/technology/2022/jul/08/elon-musk-twitter-deal-legal-consequences) gives us ‘Musk’s withdrawal from Twitter deal sets stage for long court battle’ to be honest, I am not convinced. In my mind Elon Musk needs to win and he SHOULD win. The premise is seen with ““For nearly two months, Mr Musk has sought the data and information necessary to ‘make an independent assessment of the prevalence of fake or spam accounts on Twitter’s platform’,” Musk’s team stated in the letter. “Twitter has failed or refused to provide this information.” The data in question centers on the number of spam accounts on the app, which Twitter has claimed make up about 5% of more than 200m users but Musk believes is higher.” There is the setting. You see, I personally believed it was close to 20%, a friend of mine has data showing it to be well over 40%, he stated close to 50%. This is not speculation. HE HAS DATA! That should be seen as evidence. The trolls in the EU, Russia and China, the click farms progressing the needs of wannabe’s, politicians and fake information spreaders from the Trump elections, the Covid misinformation settings, the Ukraine war. These are not done by one or two farms, this is done by thousands of players all wanting to grab a piece of the revenue pie. Twitter states that it is a mere 10 million people. I disagree, the elements I mentioned makes it well over triple of what Twitter claims. As such they are intentionally setting a fraudulent price to a product that is overpriced and the media knew this, they have had the largest part of that evidence under their own fingers. FoxNews gives us “NBC News Senior Reporter Brian Collins discovered Vladimir Bondarenko and posted about him that, “He’s a blogger from Kiev who really hates the Ukrainian government. He also doesn’t exist, according to Facebook. He’s an invention of a Russian troll farm targeting Ukraine. His face was made by AI.”” Do you really think that such a ploy is used for one account? Russian troll farms have been all over this and they have been over a few other things too. That friend of mine has data going back years. 

And it gets to be worse. You see there are trolls and click farms and the media has done very little to dig into the amount of either version, they have gone out of their way to avoid clear investigation. Even as some research it and some of it remains debatable. One source gives us ‘19.42% of active Twitter accounts are fake or spam: Analysis’ My issue here is that I do not know the source, hence I do not trust the source (whether valid or not). Consider the Twitter claim. 5% at the most, that implies that a mere 10 million are fake. Now consider the elements I mentioned earlier, there is no way that this matches up. Now consider that Twitter deletes a million fake accounts a day and this has been going on for a while. Now consider that we can not find any clear information on how many NEW Twitter accounts were created in 2021 and 2020 (or 2019 and 2018). That is important information, especially if well over 60,000,000 accounts were deleted in 2022. I believe that this shift is large enough for Elon Musk to start the case, when he gets the data from places like Trollrensics he might have enough to bust the Twitter deal. The setting is and always was that Twitter claims that at most 5% of the accounts are fake, I believe it too be a lot higher. I never speculated the numbers that Trollrensics have, but it is my speculation versus THEIR data, as such they win.

I believe that it will prove the case for Elon Musk.

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Referring to the previous

OK, this time it is not merely a case of Microsoft people (the non thinkers). I also left a piece out of the previous article. In part because I thought it was self evident, in part because it is a little harder to explain, not harder exactly, there is a lot more to this than meets the eye, but those in this environment will get it fast enough. You see, my solution might not help Elon Musk, he doesn’t have enough time. Even if his Tesla Mobile department gets cracking, he might not get the minimum numbers to convince a judge (I reckon 9%-11% would do the trick). First we need to look at a specific population. 

This is a representation of a fake account population. For arguments sake I kept an even distribution (which is not the case). The top segment are governments, really clever hackers and a few others. We won’t be able to get to them. Then we get the clever click-farms and trolls and last the eager beaver click-farms and stupid trolls. It’s the lower two segments that matter, the lowest tier is the easiest to get to, but will need work. The other one needs a lot more work and that is the path others have not trodden on (at least I think they have not).

In this there are two groups click-farms and trolls. We can get to a lot in the same way. A click farm get the revenue, by clicking (yes, it is that easy), the problem is that they need 10 clicks for a cent, as such China has a lot of these farms. People pressing buttons one after another. But here is the little surprise. There is a method, there are paths we can use to find them, and the lowest group first. There are all matters of ways that some hunters go through, because they have specific targets. In this case we also have targets, but the lowest group, it also means the most work. 

The simple click farm has one text. We need to find the first text that goes to any click farm, when we have that (from experience we know where the recipient is), so we know the text. Now we need to backtrace as much as possible and find EVERY transmitter (clicker) of that message. We do that by seeking 90 seconds before and 90 seconds after and seek the system for that text. Depending on how fast the click-farm is, we could find 200-300 click mobiles in that time, if needed we extent to 30 seconds in both direction. Now we have our first cluster. We can seek and capture these identities and set them in a database. The slightly more clever click-farm will do this with a collection of tweets (as such I showed you 3 text icons). This is also important. You see one cluster is fine, but we need a hell of a lot more, but we get a little help from the people at the click-farm, they tend to be lazy (or greed driven) so the more they transmit, the more money they get. As such we then seek who had these three messages in succession. Here we need to filter, some recipients are gullible and take anything that this click-farm sends out and some click-farm have recipient clusters. The salesperson often has a story to tell, but he’ll take any listener (even the useless ones). So we need to distinguish between the two. The recipient farm often does not send forward, some do. But now we start shaping an image. An image and a message path (a pattern) and these paths are not always the same, they can sent the covid misinformation one day and Russian propaganda the next. But in this way we get at least a dozen clusters. The problem is that this needs one hell of a server and optionally a rack of servers seeking in different regions. So Musk will have the hardware and he has the people, but does he have the time to get this all done? (perhaps he already did). And the third path is to engage with a click-farm to send your own message which you seek online. When you get that cluster you can seek what else came from there and then you have a nice setting to compare. You see, Twitter is about engaging an audience, the click-farm does not care so they are actually more exposed then others. Then there are the the click messages that hand over the #FF statement. It is risky, but the lower tier does this to get results faster and as such we get a node of connections and all connecting to clusters. 

I reckon that this approach could bank up to 20%-25% of the fake account, showing that the Twitter idea of 5% was a joke from day one and has been so for years. There are of course a few more ways to get there, but revealing them would also show the hand to the more clever click-farms and that is what we are trying to prevent. I reckon that it should be possible to get 90% of the green group and 30% of the yellow group. In this I set the graph to reveal equal groups, but the green group is 20% smaller and yellow group is at least 40% larger. The red group is relatively small, and does the most damage, but that was not the exercise, it was to show that the Twitter claim of 5% fake accounts was folly (from the start mind you) and I reckon that this could be relatively easy to show, but to get these numbers takes a serious amount of server power. It would even be better to set the results in something like IBM Modeller or Palantir Gotham to see where else the data leads, because that would become the next task, mapping the disinformation streams and how it is distributed. Even if these people do not break any laws, they are helping and propelling disinformation, optionally endangering their own nation and that too needs to be known. There comes a point where the right to be stupid is no longer an excuse.

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Meme by Elon Musk

The guardian is giving us a part, other papers are giving us a part. Yet no one is treading on the side where they have to be, the media pussies on patrol. Trying to keep safe their digital dollars. And it is about to come to blows. You see the article ‘Elon Musk may have to complete $44bn Twitter takeover, legal experts say’ (at https://www.theguardian.com/technology/2022/jul/10/elon-musk-may-have-to-complete-44bn-twitter-takeover-legal-experts-say) gives merely part of the painting. Yes, legal experts state “Quinn said Musk’s information requests on spam accounts were not “reasonable” and would not be accepted by the court. “He can’t use unreasonable information requests to create a pretext to claim a violation,” he said.” But the setting is incomplete. Twitter has maintained that no more than 5% of the Twitter accounts were fake, I have data suggesting it is as high as 20%, another source (www.trollrensics.com) has data showing the number of fake accounts for trolls and misinformation to be as high as 50%, this implies that Twitter is trying to sell a bill of goods, but the bill is only 50% filled and that has been at the centre of this all along. So whilst Jack Dorsey and friends and now in a stage where the gig is up, they need to get as much out of it as possible, because the media will at some point ‘wake up’ and take a much deeper look. Consider hundred of media outlets and they have been avoiding this part all along. Politicians setting their premise, misinformation on covid, election misinformation, and the Ukraine war thousands to troll accounts working day and night to give a false premise of what is going on and in all this the media remained SILENT. 

Trollrensics has data spanning 8 years (at least) and that is merely the beginning. You see, on route to home I remembered that trolls and click-farms rely on greed. As such we see a different setting. First there is the ‘unmonitored source’ that gives us “Twitter doesn’t reveal IP addresses of its users. They use it internally and strictly restrict the public from this information. But there’s always a way. In this article, we’ll discuss how to find someone’s IP address on Twitter.” This implies that we need another path, but criminals and click-farms are lazy, they will reuse what they can. Every second they can tweet is another few cents in their wallet, as such more is better. This implies that if you create a database of the @TwitterAddress and you strip all the messages, you can look per message and see how it moves. This is not a simple solution, you need serious computing power for this. But as such, you get a message that is spread (in the near same instance) from different mobiles in the same location you optionally have a click farm point. Now if we get a multitude of misinformation from clusters of mobiles, we have found such a place. 

This is a mere setting to get to the numbers. You see, Russia and China have hundreds if not thousands of these click-farm locations. And now we have a serious number, when we move that action from nation to nation, we get well beyond my 20% and way past the 5% claim of Twitter. When that is obtained, we get what might be considered evidence towards what some would call the alleged fraudulent sale of Twitter to Elon Musk. Why Fraudulent? Well, Twitter maintained that they have no more than 5% fake accounts. These numbers would prove them wrong and with the previous part that they had IP addresses they had the information a lot longer than anyone would care to speculate on and as they speculatively lived by the rule that they look sexier with 330 million active users, than with 120 million active users. And one source gives us “Twitter has some 330 million monthly active users (MAU) based on its last reported data that leveraged this metric in the 1st quarter of 2019. As of 2020, Twitter’s monetizable daily active users (mDAU) stands at 166 million, which represents a 24% growth from 2019.” In the middle of Covid Twitter grew 24%? I am not saying it is not possible, after all Amazon pulled it off, but how many stores were active during coved? In addition to this, where did these funds come from? In all the presidents men we hear ‘Follow the money’, that equally applies to trolls and click-farms. They got paid, they paid for things, that money trail is equally important in discovering what was what. It is not fool proof, because others use similar paths for valid reasons, but that is one person, one business. Not a person or business with hundreds of phones. 

All this should have been seen and looked at by the media years ago, but it wasn’t interesting is it not? And as for the meme, see below. When you consider the elements of the meme, the silence of the media makes even less sense. Yet, I leave that to you to look into. 

Meme by @ElonMusk

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You might not like it

This morning I got confronted with an article so in my face it literally made me stagger. This article came from the brilliance of a YouTube element called ‘Veritasium’, it was all about the dangers of Facebook, moreover, the dangers of actual investing in Facebook. Of course the hilarious part that I read this on Facebook, so we see a slightly additional view when we consider winking at social media. Yet, before I start we must not forget to show divine humour by emulating the platypus. If Pig Latin is a reconstructed language and we see the Dutch expression of Fisherman’s Latin as ‘catching that really big fish’, is Veritasium a new elemental truth or a truth in mere reconstructed elemental words?

The article is about the concept of buying ‘likes’. The link is here (at https://www.youtube.com/watch?v=oVfHeWTKjag) and I can tell you now that it is worth seeing from beginning to end. The movie takes 9 minutes and it gives you the low down on buying likes and more important the dangers of pushing your visibility.

Now it is best for the reader to see the video, because the speaker does so in a very eloquent way and the only way to get his point across is to quote his entire article, which no longer makes this MY article.

I have seen these issues before, why do we need to ‘like’ things? I do, there are a few likes on my list, I see some advertisement on Facebook and as such, I have no real issue with the advertisement part. It is the price for a free Facebook. Yet, why would you want to pay for more likes? It seems that apart from it costing you money, the video shows that paying for likes is ultimately bad for business. This is only the top of the equation.

The video is calling into question other issues too. The fact that one issue made them shed 83,000,000 fake accounts, one might wonder how many fakes there really are on Facebook. The second is that the linked algorithm is also in question. If we consider the data linked here, then we see a different issue, which links them all.

  1. How are bought likes regarded, especially in the approach towards a percentage of linked advertisements through connected friends?
  2. How can we get actual advertisement pushing flagged towards an engaging audience (which shows growth and possible commitment), while we know that bought likes will never be engaging likes.
  3. How is the data cleaned to show a better mapping of audience versus engagement as well as geography versus interest?

The last point is not just linked over the two issues, we see in the video that there are profiles in the system that are there to like ‘everything’, not in a Zen way, but in a way to mask fake accounts, which gives another matter regarding the algorithms and how they could likely be fooled by those who understand the system.

Yet this is not just about Facebook, we should now also look at the medium that gave us this treasure, namely YouTube. You see, YouTube is all about creating hypes, vibes and types. It is the last of these three that have been a worry for some time and there is no indication that this will stop anytime soon. So, what happened? It started a few months ago, I was looking for a game trailer and there it was right in front of me, the movie Silent Hill. Now, as I am between apartments, I have no access to my DVD collection, so watching this one was heaps fun. I was just a little upset that a Blu-ray was never released of this movie. So, as the month progressed I started to dig into this phenomenon as this seems to be a copyright violation. As I started to dig deeper into this, I noticed a league of movies, some extremely recent all watchable. Even 2014 movies like Godzilla and the new X-Men movies were on YouTube. Now, there is at times a massive drop in quality and 1-2 were clearly filmed in a cinema, but the movies are there. But it is not all that clear. In some cases there are hundreds of copies yet none of them have a movie, they just have a link to click on, or the weird text ‘this movie was deleted by YouTube’ (if that is so, why is the file and the entry still there), so YouTube is used in a growing league of non-trusting reasons. Yet, is the approach for marketing or criminal reasons? That is also the issue, because it tends to skew the people who go there and the reason why they went there. I can very much understand that there are scores of Bollywood movies there, but are they any less a case of copyright infringements?

How does the YouTube issue relate to the Facebook issue? It seems to me that the second is an automated form of getting people to do for the click farm so that they go undetected for a lot longer. Consider the effort it takes to add 100 copies of a film, people might want to see and they all go to a link (for implied free download), we now have a person losing possibly up to 15 seconds, whilst they at that point are facilitating the work of a click farm. The farm remains less detected, whilst the farm gets 100% of the revenue from the click. Now consider that when these movies come out over tens of millions will try a few links, so these farms get all those attempts for a mere 100 uploads, it seems like this is easy money. So as we consider that Google, Yahoo, MSN and others are now trying to battle these farms more and more, yet the fact that YouTube (a Google child) seems to have kept the backdoor open, should be a massive issue, because this puppy tends to go straight to Facebook and Google+, which leaves the impression that people were mopping the floor whilst the tap remains running. So nobody is going anywhere fast.

As such my question now becomes, how anyone can proclaim that keeping the status quo in these matters is nothing less than running backwards on a highway patrolled by blind drivers. So, here is the kicker, how come Google has been unsuccessful to stop such levels of copyright infringement? In my view there are two options, they either are unwilling or unable to do so, unable means that they are not clever enough and that their system of facilitation is there to keep them non-accountable, if it is unwilling, then we see another version where their lives (Facebook and Google) revolves around bandwidth, which gives us the old Telco revenue issue. It is all about the money!

You might not like that reality, but it is a reality we all helped create. What a difference an algorithm makes!

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