Last year, Aaron Hertzberg compiled an idiot’s guide on how to convince the masses that there is a deadly pandemic, when there isn’t one, and pretend there are no injuries caused by the vaccine, when there are.
He has written the text for aspiring propagandists who would like to learn the art: “For the beginner, [the art of propaganda] can be very difficult to master. Even the experienced propagandist can at times fall into the trap of thinking that creating and disseminating propaganda is a straightforward enterprise – which is a good way to win a permanent all-expenses paid Siberian vacation,” he said.
“The following short guidebook will provide the aspiring propagandist, WEF lackey, Communist Apparatchik, Woke Marxist and seasoned government bureaucrat alike with the tools and knowledge necessary to develop their promising talent into full-bloom mastery of the art of propaganda.”
As one can imagine, Herzberg’s guide is necessarily long. We are publishing one section at a time so hopeful propagandists don’t feel overwhelmed and give up on their dreams of a career in propaganda after the first hurdle.
By Aaron Hertzberg as published by the Brownstone Institute on 20 December 2024. (The article was originally published on Hertzberg’s Substack page on 15 June 2023.)
Take a close look at the above slide from an international poll conducted a few months after covid struck: This is what effective propaganda looks like. And the true effect was even greater because the “real world” numbers used to calculate how badly people exaggerated the risks of covid were of course themselves derived from … the world’s preeminent propaganda organisations (masquerading as public health agencies). Who were themselves already wildly exaggerating the risks of covid.
The art of effective propaganda is an encompassing discipline that requires careful and thorough study – and review – from time to time. For the beginner, it can be very difficult to master. Even the experienced propagandist can at times fall into the trap of thinking that creating and disseminating propaganda is a straightforward enterprise – which is a good way to win a permanent all-expenses paid Siberian vacation. It is not usually so simple a task to befuddle the entire society every day, 365 days a year, indefinitely.
The following short guidebook will provide the aspiring propagandist, WEF lackey, Communist Apparatchik, Woke Marxist and seasoned government bureaucrat alike with the tools and knowledge necessary to develop their promising talent into full-bloom mastery of the art of propaganda.
This book is a bit long!! So don’t feel as though you must read it from start to finish in one shot, for that is a recipe for burnout and to not retain the critical information contained within.
This manual is divided into the following sections:
- Section I. Definitions – How to redefine words, terms and metrics to keep them in line with the regime narrative.
- Section II. Curating Data – How to hijack the processes of recording, reporting and publishing data.
- Section III. Vetting which data are considered to be part of Official Science – How to vet and data and dispose of regime nonconforming data so it never appears in any Official Science or regime datasets.
- Section IV. How to rig a study – Exactly what it sounds like.
- Section V. Doctoring the datasets – Sometimes, you will need to go in and do a little data ‘surgery’ to modify the content of databases that contradict regime talking points you can’t simply eradicate.
- Section VI. Control the standards of evidence – How to set up a hierarchy of evidence that puts regime-friendly Science at the top and regime-unfriendly science at the bottom (of the Mariana Trench).
- Section VII. The Ecclesiastical Authorities of Science – How to ensure that Scientific Authorities reliably parrot the regime’s facts and narratives.
- Afterword – Tying it all up nicely, like one of Peter Hotez’s bow ties (he’s a particularly grating Regime Celebrity Scientist).
[Note from The Exposé: Because Hertzberg’s ‘Idiot’s Guide to Cooking Data for Aspiring Propagandists’ is so long, we are publishing one section a day over seven days. If you wish to read ahead, you can follow the links to the sections as provided above or read the entire guide on the Brownstone Institute’s website HERE.]
Table of Contents
“He who controls the language controls the masses.”— Saul Alinsky, Rules for Radicals
How we define concepts or categories determines what tidbits of the real world they communicate or represent – or what they don’t communicate or represent.
Malleable definitions, and an arbitrary and capricious standard for assigning definitions, are an absolute must for any effective propagandist. Despite the best efforts, even seasoned, expert propagandists will inevitably confront situations where the curated data that exists, or people’s lived experiences, are problematic to the official regime narrative.
Effective propaganda therefore requires the capacity for nimble and highly adaptive flexibility to control the content of data, especially pre-existing conventional metrics that the public is accustomed to hearing about which are notoriously difficult to simply make disappear (unlike the ease by which you can vanish a dissident scientist off of YouTube or Facebook). For example, you won’t be able to avoid talking about “deaths” in the context of a novel Dreaded Disease Pandemic – the primary way people will relate to gauging the severity of a disease will always be first and foremost: “How many people died from the disease?” But you can change what “death” refers to in the context of the novel Dreaded Disease if you want to increase or decrease people’s sense of how deadly it is.
In practice, this means that when the normal understanding of a term or concept shows that the reality does not quite fit the regime’s desired narrative, just change a few definitions and voilà, problem solved.
As many a prominent communist propagandist throughout history has also observed, “He who controls the language rules the world.”
There are a variety of ways to alter or transition definitions from problematic to acceptable.
I-1. Limit a Definition
If the conventional definition of something includes concepts, data or information that is at odds with regime dogma, limit the definition so it no longer includes the unwanted information. There are plenty of ways to do this. So, we’ll list a few of the more common types of characteristics you can use to effectively limit a definition:
Limit the definition by time interval: Suppose that vaccinated people get the Dreaded Disease at very high rates in the first 30 days following vaccination and after 90+ days from vaccination with the Glorious Vaccine. This is a big problem because people will think that the Glorious Vaccine is not effective:
The red line shows the case rate per million people after getting vaccinated with the Glorious Vaccine, by the number of days since vaccination. As you can see, in the first 30 days, the rate of breakthrough infections is very high, but between days 30-90 the case rate is practically 0, and after day 90 the case rate starts climbing again.
In plain English, what you see on the above chart is that the number of cases per million people goes as follows:
- Before vaccination: 500 cases of Dreaded Disease/million people
- 10 days after vaccination: 3,000 cases of Dreaded Disease/million people
- 20 days after vaccination: 1,700 cases of Dreaded Disease/million people
- 30 days after vaccination: 100 cases per million people
That’s very inglorious efficacy for the Glorious Vaccine – something that cannot be allowed to stand. One solution is to simply change the definition of “vaccinated” to mean someone who is between 30 and 90 days after being injected with the Glorious Vaccine – in other words, anyone who is within 30 days of being vaccinated, or after 90 days from vaccination, is not considered “vaccinated:”
This particular tactic was pioneered by pretty much every public health agency in the civilized world, where the definition of “fully vaccinated” for the covid vaccines was limited to “14 days after your second dose”:
Limit the definition by quantity, such as the number of exposures: For instance, if a bunch of people who received 1 dose or 5 doses of the Miraculous Treatment Mirafaucivir died (the first dose kills off people who are particularly susceptible to its toxicity, and 5 doses is too toxic for pretty much anyone), limit the definition of “treated with MiraFaucivir” to between 2-4 doses:
Limit a definition by adding absurd conditions into the definition that are almost impossible to fulfil: For instance, you might try using the following conditions to limit the definition of a “vaccine death” in the context of a mass vaccination campaign with the newly-minted Glorious Vaccine:
It’s pretty hard to ever manage to get a “confirmed” case of someone dying from the Glorious Vaccine under conditions like these.
(You must remember to obstruct autopsies as much as possible to make this example definition fully effective.)
I-2. Expand a Definition
Conversely, sometimes you may want more of something than there actually is. Expanding definitions is a great solution – just reverse the above instructions for limiting definitions.
So, if you need more deaths from the Dreaded Disease than there are people actually killed by the Dreaded Disease, you can expand the definition of a ‘Dreaded Disease Death’ to “any death within 30 days of a positive test,” and just like magic, you have a full-scale pandemic on your hands.
To illustrate this, suppose that after 12 months of Dreaded Disease circulation, only 7 people per 100,000 infections were actually killed by the Dreaded Disease – not exactly scary. You pull a little switcharoo and expand the definition of a ‘Dreaded Disease Death’ to something like what the CDC pulled – “any death within 30 days of testing positive for the Dreaded Disease.” Since plenty of people die every day, if you mass test them all, you will inevitably “discover” a whole boatload of dead people who happened to have the Dreaded Disease when they died, even though they were killed by something completely unrelated like cancer or a car crash. Look at what a difference this makes:
New York State offers a classic illustration of how to expand the definition of ‘Dreaded Disease Death’ to create the appearance of a once-in-history super-duper scary apocalyptic pandemic – just look at the following gorgeous open-ended definition for a “probable” covid death:
NOTE OF CAUTION: You must always take care to NEVER, EVER, EVER – EVER!!! – articulate to the public how you’re gaslighting them in clear, concise language they can understand. The following unforced error in 2020 from Illinois Public Health Director Dr. Ngozi Ezike is the sort of thing that gets you a quick one-way ticket to the Gulag – she actually said the following at a public press conference (see embedded video below):
“So, the case definition is very simplistic. It means at the time of death, it was a covid-positive diagnosis. So that means that if you were in hospice and had already been given a few weeks to live and then you also were found to have covid, that would be counted as a covid death. It means that, technically even if you died of a clear alternate cause, but you had covid at the same time, it’s still listed as a covid death.”
She was doing the right thing of course by using such a wonderfully expansive definition for covid deaths, but she stupidly and carelessly let the cat out of the bag for the whole world to see. That’s the kind of careless blunder that can demolish an entire propaganda campaign overnight. And also, the kind of thing that can be a career-ender (or worse):
I-3. Invent a Brand-New Definition
Sometimes it is simply not possible to hide the common understanding of something by merely playing with the definition at the margins. In that case, you can take the gutsy step of redefining a word, concept, or category altogether to fit your propaganda needs. Just beware that it may be a tad more difficult to convince people that the old definition is a figment of their imaginations.
Take the CDC (yup, we’re gonna be quoting CDC a lot; they are the preeminent health propaganda organisation in the world after all), which changed the definition of “vaccination” multiple times over a span of 6 years:
(Sidebar: The above tweet offers a lesson in the need to control rogue legislators who might try to dissent or even expose your propaganda efforts. You don’t need the additional headache of dealing with clear evidence of your linguistic treachery broadcast to the public from the floor of Congress or Parliament (or the even bigger headache of being banished to Siberia as the fall guy for allowing such a thing to happen).
On occasion, you may even find that you are trapped by the ordinary conversational meaning of words, where they highlight something you can’t afford people paying attention to. Should this occur, you will be forced to implement a fundamental change to the very essence of the language. This is a sort of nuclear option for when you can’t hide something any other way, and also can’t afford to not hide it. (Beware!! Such an audacious endeavour comes with a significant degree of difficulty as many people will be inclined to resist such open and bold language transitioning – similar to how many unenlightened Luddites resist going along with gender transitions).
For example, take the term “peaceful protest”:
Of course, ‘limited’ is a subjective term whose precise contours are ill-defined, which gives you a lot of latitude to apply the description to almost anything regardless of how incoherent or misfit the application is, as evidenced by this real-life media report that needs no further description:
I-4. Combine Categories
Sometimes, it just isn’t practical or feasible to mould the data simply by changing definitions. Not to worry though – if you can’t change the definition, you can instead change the datapoint or category itself that folks are used to the word or phrase referring to. People aren’t attuned to subtle or nuanced differences in categories or data points, and the media helpfully conflate most things anyway, making this an easy and convenient trick. For instance, you can try:
Combining different age groups:
Suppose the Glorious Vaccine is causing a bunch of kiddies to turn into zombies. That’s pretty bad for the regime. (Which means you should reassign a few scientists to work at a climate research station in Antarctica for the remainder of their careers. Without socks.)
First, you must always refer to this novel condition as “Safe and Effective Transformation into Carnivorous Zombie.” The reason for the carnivorous part is simple: ‘flesh eating zombie” sounds too scary, and plain “zombie” feels like the zombies are basically dead – i.e. the precious kiddies are dead – neither of which is an impression you want people to come away with. (Even though our hypothetical example here is unlikely to materialise in practice, the principle is relevant and applicable to any situation: you must always name something in a way that conveys a sense of what you want people’s impressions to be.)
Second, because the rate of Zombification in the 12-17 age cohort is so high that it is obvious to anyone who looks at the data (below chart), you will probably have to deal with that. So instead of presenting the data broken down by age, where people will immediately notice the surge of kiddie zombification, present the data as a combined age group that is big enough to hide or launder out the signal:
What you are doing in essence is taking the term “rate of Zombification after the Glorious Vaccine” which can be used to refer to the various different age groups and making it refer to the rate of all age groups combined.
Now nobody will notice that the data shows a clear risk to kiddies of being turned into carnivorous zombies by the Glorious Vaccine.
Or conversely, supposing that the youngsters aren’t dying from the Dreaded Disease at high enough rates to scare the mommies, you can present Dreaded Disease death data from a combined age group of 0-50 that makes it seem like there are soooo many deaths from a group that includes the kiddies:
Combining different demographic cohorts
Same idea as the age groups; suppose you need to avoid the citizens figuring out that the Dreaded Disease is really only dangerous to the morbidly obese people – which is bad:
- Firstly, because then they won’t be scared of the Dreaded Disease.
- Secondly, because people might start to question whether fat is healthy, which you can’t allow because they might begin to question the regime narrative regarding ‘fat positivity’ and then who knows what else afterwards.
So, you should just present the Dreaded Disease death data using a combined category that covers all types of weight identities:
Combining different time periods
Suppose you notice that the deaths from the Dreaded Disease are decreasing month over month – which can be catastrophic to regime plans that require the people to believe that the Dreaded Disease Pandemic is in full circulation for another few months. If the people get the idea that the Dreaded Disease is winding down, well, that’s a lot of lost opportunity to use the Dreaded Disease crisis as a means of effecting societal transformation to consolidate and solidify the regime’s power.
So instead of presenting the death data by month, combine all three months into a new category of “monthly average over the three months” which will mask the decrease from January to March, illustrated below:
Combining different geographical jurisdictions
Suppose that there’s a rogue state within the country that is making problems for the regime that doesn’t follow regime guidance for dealing with the Dreaded Disease, which we’ll call Death Santistan. If they show better or even equal results to the rest of the country where they are good citizens and follow regime guidance, that would be pretty bad. Suppose further that there’s a city or county within this bad state that is a loyal regime county following all regime guidance but whose death rate is much higher than the rest of Death Santistan. Which is very very bad. Solution? You can present data from the entire state so people can’t tell that the loyal county following regime guidance has a death rate 10 times the rest of the state. There’s even a bonus benefit: you can point to the whole state of Death Santistan as a failure because the loyal regime county will make the whole state look much worse!!
Combining all the cities and counties in a disloyal state to hide the problems unique to loyal regime cities is one of the go-to propaganda tactics used to try and hide unflattering information such as the vastly higher crime rates in regime-loyal cities compared to cities controlled by the evil opposition.
(Sidebar: High crime rates are a good thing of course that is a deliberate choice of the regime by design – high crime rates are useful for the regime because instability makes people more willing to accept tyrannical government as a solution.)
To illustrate, here is a brilliant piece of gaslighting from one of the mainstay regime media mouthpieces:
Look at the subtitle in the crimson box – see how they adroitly finger the red states for the high crime rates that are all in the blue cities within the red states but not in the rest of the state where the governance is “red”? Exactly.
Combining different types of the effect or phenomenon.
For instance, if there is an increase in a specific subtype of disease condition – like alarming increases in rare cancers following the rollout of the Glorious Vaccine, which might make people question the official regime narrative that the Glorious Vaccine is the safest entity ever created or discovered in universal history – you can use the general category of cancer – which is 1,000x as big – to hide the signal.
Another way to think of combining categories is that you never give out the specific data for different groups or subsets, something that was pulled off to absolute perfection when covid struck. Consider the following polling results, showing the share of covid deaths for each age group side by side with the percentage of each age group who were worried they would be killed by covid. (The blue bars show the percentage of each age group who were worried about getting killed by covid, the green bars show the percentage of the total number of covid deaths that were in each age group.)
Had people understood what their actual risk of dying was, the blue bars should be at least in the ballpark of the green bars. When the blue bars are dramatically higher, that is the result of brutally effective propaganda by combining all age groups into one category without ever differentiating:
Smashing success indeed!!
I-5. Split Categories
Sometimes you will need to split up a category instead of combining it with another one. Just reverse the framework laid out above for combining categories.
This neat little manoeuvre is especially useful when you need to get something below the threshold for statistical significance. Since statistical significance is a pretty important concept in data and Science, it’s a good idea to explain how this works.
Statistical significance as used in conventional medical academic or scientific language basically means that the likelihood of something not due to random chance is less than 5%.
If you flip a coin 10 times, the odds of getting 7 heads because of random chance is 11.72% – NOT statistically significant. If you flip a coin 100 times, the odds of getting 70 heads because of random chance is a minuscule 0.0023% – VERY statistically significant (cuz that’s much less than 5%) – meaning that it is not reasonably attributable to random chance, rather something specific (like cheating) caused the coin to flip 70% for heads.
Why is this? To get 7/10, all you need is two extra coin flips to go your way – going on a bit of a streak. Small deviations like this can easily happen at random. However, to get 70/100 requires 20 extra coin flips to go your way – the odds of getting *20* extra coin flips out of a total of only 100 by random chance are negligible. So, if we see 70 heads out of 100 flips, we can presume that there is some kind of cheating going on, because that’s very very unlikely to happen by random chance.
You can use this to your advantage to divide and conquer a statistically significant signal – you can divide a category where there is a statistically significant signal for something against regime doctrine into smaller categories in order to break up the signal from a “70/100” into a bunch of “7/10”s that are individually not statistically significant.
So, if, for example, there is a signal that there are more deaths per 100,000 per year after the Wondrous Glorious Vaccine Campaign, you can publish the death data broken down by age group where no one age group will show a statistically significant increase in deaths (and you can claim that it’s probably leftover excess death from “Long Dreaded Disease” from complications of getting the Dreaded Disease):
Note of caution: This particular tactic should ideally be combined with something else otherwise people could reverse-engineer the breakdown by doing a bit of simple arithmetic to add all the age groups together. So, make sure to add in other confounding tricks.
I-6. Redistribute / Redraw Categories
A more finely tuned alternative to combining categories outright is to redistribute them – to redraw the lines so to speak. This can be done using any characteristic by which categories are differentiated.
To illustrate, returning to our example of the evil disloyal state of Death Santistan, instead of combining the entire state into one statewide statistic, you can surreptitiously redraw the geographic boundaries of the counties inside the state for the purposes of Dreaded Disease data like this – look at what happens when we change the county borders to the green lines:
Note: This does not mean you have to literally redraw the counties for political and other purposes like voting districts; all you’re doing is using different borders for the sole purpose of Dreaded Disease statistics. (The population however will assume that you mean the actual counties that exist and will therefore not realise you pulled a fast one over them. It’s called propaganda for a reason.)
I-7. Fluid Definitions
There are times when you may have the paradoxical need to use a specific definition for one thing but must also avoid that specific definition for something else. For such cases, you must act like a dictionary – dictionaries typically have multiple distinct definitions for one word, you can do the same.
For instance, the word “woman” is sometimes defined as “an adult human that possesses female anatomical and genetic characteristics,” such as when discussing a woman’s right to choose; and is sometimes defined as “a person who identifies as woman,” such as in the context of organised sports.
Source: https://expose-news.com/2024/12/22/an-idiots-guide-to-propaganda-part-1/
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