Another angry woman

After writing about the digital advertising grift yesterday, I got some really good followup questions regarding agencies using data during election campaigns and the Brexit referendum. Weren’t Russia and Cambridge Analytica and all those bad-faith actors using microtargeted online content to shift elections in the direction of their guy?

There was certainly manipulation going on. But the significance of microtargeting was not how the sorcery happened.

Consultancy firms like Cambridge Analytica can mostly be categorised as companies whose product is that everyone thinks they could spy on you, using your social media data alone. They don’t actually do this. Even in the data scandal, the profiles that Cambridge Analytica had constructed were built using personality quizzes and a hefty dollop of pseudopsychology. They then extrapolated this to other users with absolutely no evidence that any of these segments held up. It was lucrative and it sounded very fancy, and a lot of the worst people in the world paid good money for this.

Cambridge Analytica’s brand is cartoonish villainy, and it is very popular among cartoonish villains. They were paid vast sums to run microtargeted ads based on their clever profiles they’d built up. In order to do this, they’d have had to translate their clever profiles into the demographic details which are used to target ads.

So ultimately, what they were doing after all this was… “hey let’s show this ad about how Brexit will save the NHS to 55 year old dental receptionists in Stoke-on-Trent”. In other words, the same way anyone else targets ads, despite the science-y veneer.

How to actually steal an election using segmentation

Segmentation is a great buzzword for sorting people into categories, and its importance is wildly overstated in order to sell tools for audience analysis. Nevertheless, it is helpful, when stealing an election, to segment your audience into three groups. You don’t even need to do much analysis on these groups: two out of the three will reveal themselves pretty quickly.

The three segments required for stealing an election are: people who are voting for your guy anyway; people who will never in a million years vote for your guy; and people who are on the fence.

You don’t need to microtarget these audiences in any way; in fact, you need to target all of them. You need a message which resonates with the people who will vote for your guy; make the people who will never in a million years vote for your guy object to the point of insufferability; and that the thing which is most noticed by swing voters is the insufferability of the second category.

A good example of this is Brexit. Pro-Brexit messaging was incoherent, but resonated in positioning the anti-Brexit advocates as elite and out of touch. Unfortunately for the most prominent anti-Brexit voices, they were pretty out of touch, and reacted with complete insufferability, and continued to be patronising, insufferable, snotty, cliquey and generally awful, until the swing voters were sufficiently icked out by this behaviour. I very grudgingly voted Remain, but considered appending my ballot with a short essay as to how this vote should not be counted as an endorsement of the Remain campaign.

Why use microtargeting, when you can just wind your rivals up to the point of being so fucking annoying that they become an electoral bonerkill?

You can apply this to most elections. There’s a side that’s awful, and a side that, by positioning, appears less awful. The swing voters will probably come down on the lesser of two awfuls. It’s a tale as old as time, and it works. Also, it doesn’t require any real data analysis, you just have to make the other side look worse.

So what are they actually doing to steal an election?

We actually know a lot about how the sausage was made with Cambridge Analytica, because their CEO was caught on camera talking about how they stole elections. Despite the major data breaches, Cambridge Analytica favoured doing things the old-fashioned way: bribery, honey traps, information gathering using sex workers, entrapment. Even the unusually candid CEO in question couldn’t provide evidence that they’d used social media data to steal the 2016 US election in his bragging – he said it was “self-destructing”.

The old dark arts are tried and tested, and these are what have been so successful at stealing elections in recent years. It’s time to take off the mask and do a Scooby Doo reveal of the real villain, hiding underneath the concerns about digital microtargeting…

It was journalists all along.

In the 2016 US election, Donald Trump received more media coverage than any other runner in the race. In combination with this, Hillary Clinton got some coverage, and it was pretty much all about the emails scandal. Nigel Farage’s stupid frog face was all over the news, all the time during the Brexit referendum. And don’t even get me started on the absolute state of the four year campaign of hit jobs on Jeremy Corbyn.

Mass media is more than sufficient to tip the scales. You just need to throw in a good wedge, and this is easily done the old-fashioned way: by throwing shit at the wall and seeing what sticks, then watching everyone fight each other. Sure, you can supplement the fight with some bots, if you want, but the humans will be more than sufficient.

But we need a bogeyman here, especially as journalism is getting markedly worse and worse. And so the bogeyman can be social media witchcraft. It’s not the carefully-curated PR attacks that are the villain, it’s Facebook ads, who know exactly when you’re constipated and will trick you into voting Lib Dem while you’re on the toilet.

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Digital advertising is a scam from top to bottom. In fact, it’s several scams stacked on top of each other, wearing a trenchcoat, and some of the foundations of fibs are so effective that otherwise reasonable people entirely buy into them.

Data-driven ads are anything but

I’ll start with a few examples of the data which is definitely held on me, and just how entirely bad my targeted advertising is.

Facebook know my age and date of birth. They have had this data since I signed up for the website, 15 years ago. They know exactly how old I am. They also know where I live. Hell, sometimes I used to check into places with my location on. Despite knowing I am way north of 30 and way south of Birmingham, they are incredibly keen on advertising me events explicitly limited to people under the age of 30 in the Birmingham area.

Google knew I wanted to buy a mattress. They knew this because I googled it. And I clicked through to a brand selling mattresses, and I bought myself a mattress. The brand know I googled said mattress. Google know I clicked through. From Google’s own analytics, they ought to know I bought the mattress. Since buying that mattress, I’ve been constantly advertised mattresses, especially the one I already own and they know I already own.

Some might claim that in fact the advertisers are being incredibly smart and they’re advertising me activities for women under 30 in Birmingham so I go and tell my friends who are under 30 in Birmingham to go and do that. But of course, Facebook would also know that I don’t have any friends in that demographic. Or maybe that mattress seller is trying to tell me to refer a friend to buy that mattress by reminding me that I own a very nice mattress. In which case, why isn’t it advertising the referral programme, which I know they have because I received several emails and a physical leaflet about it with the fucking mattress?

The more simple answer is that the advertisers aren’t being data driven at all. They’re ticking default boxes or casting wider nets. I’m getting advertised mattresses because I have ~an interest in mattresses~. I’m getting activities for women under 30 in Birmingham because I’m under 40 and on the same island as Birmingham.

For all the buzzwords about “data-driven” and “smart” and whatever else you want to call it, the advertisers are just going “eh, sounds about right” and letting a robot automate their job.

This, then, is the first grift in the chain. Despite claiming to their boss that they’re using “data-driven” advertising, they’re targeting their ads even less than taking out a quarter page in the local newspaper.

The product: they could spy on you (but don’t)

Everyone is rightly nervy about the sheer quantity of data that big companies hold on us. Social media companies know all about your demographic information, social connections and interests. Amazon knows exactly when you have an outbreak of aphids because you buy things to kill the nasty little beasties, and it probably also knows when you’ve had a nasty breakup because nobody listens to Fleetwood Mac’s Rumours on repeat at 3am when they’re in a good place. Google basically knows everything about you.

At least that’s the theory. And that’s the product that they’re selling to advertisers. They have an enormous dataset from which everything an advertiser could ever dream of about a person can be garnered. They’re the world’s biggest, bestest spy network, which means they have quality data to help your business be the biggest, bestest business reaching the biggest, bestest customers.

At least that’s what they say.

Actual spying requires actual spies. There’s a reason intelligence agencies are such big employers: they have all of their fancy spy computers, but they know they need to hire humans to actually deduce patterns and sort signal from noise. They’re aware that a human brain is always superior to a computer in figuring this out, so they get humans to do the work.

Meanwhile, tech companies break into hives at the thought of getting a human to do a job. Their ethos is that if a human can do a task, a machine can do that task better, and not cost them anything such as salary, pensions or or a basic level of respect. Tech companies are fatally allergic to getting a human to do a human job, so content moderation is largely an algorithm looking for the word “boobies”. A tech company would go into anaphylactic shock at the very notion of employing a human to analyse their vast dataset.

So it’s all machine learning, and the machines are very, very stupid. Have you ever looked at your list inferred interests on a social media platform? If you ever tweeted “I don’t like Game of Thrones, it’s not for me,” you’ll be classified as interested in Game of Thrones and possibly get served ads for it. These machines may also attempt to deduce your age, gender, and so forth based on half-baked crap fed into them, and it seldom comes up right. Maybe that’s why it thinks I’m under 30 and in Birmingham. Perhaps I internet in a Brummie accent.

It’s no wonder that on multiple occasions, big tech has been caught out completely making things up when communicating with advertisers, and they continue to do so. Facebook was famously found to have inflated or outright fabricated video metrics. GA4 very quietly admits that the data is padded out with machine learning. The data is a lie, and a lot of it is because they literally haven’t the first clue on what to do with it, they just need to steeple their fingers and act all evil so advertisers think they have it.

Advertisers, then, are getting served a steaming turd on a plate rather than the medium-rare filet mignon they were promised.

And meanwhile, the spies don’t even need that data, because your posts are public anyway.

But enough about that. The problem is this grift is, too, built upon a grift.

Marketing science is a grift

I work in marketing, for my sins. This is mostly why I’m so entirely down on the marketing industry and many of the people who work in it. I also happen to have an MSc in psychology – actual psychology! – with a focus on behaviour change.

On day 1 of your class about behaviour change in a science course, you learn that behaviour change is not a simple matter of information in, behaviour out. Human behaviour, and changing it, is big and complex.

Meanwhile, on your marketing courses, which I have had the misfortune to attend, the model of changing behaviour is pretty much this: information in, behaviour out.

The thing with the entire “science” of marketing is the underpinning theory base is basic common sense which has been treated with a bit of a brand makeover, turned into a couple of overcomplicated diagrams with some neologisms obscuring meaning. Digital marketing has become very popular because baked into it are a whole bunch of metrics so you have something to show your manager that you’re not spending the entire day tending your geraniums, but do the metrics really mean anything?

The metrics that marketers are told they need are marketed to them by the marketing department of a company that specialises in making products for marketers. And that company was probably started up by someone who worked in marketing.

Marketing theory is never tested rigorously. The common sense incredibly sound scientific view based on heaps of scientific evidence view – showing your ads to people more likely to buy your product is more efficient because they’re more likely to buy your product anyway – is entirely untested.

There’s an anecdote that a glitch with Facebook led to ads no longer being targeted over a period of several weeks. And absolutely nobody noticed because the metrics all looked normal, the engagement and purchasing was just the same.

There isn’t any evidence to suggest that an ad targeted to 35 year old men with children with an interest in football is any more likely to result in sales of Football Dad socks than a poster for Football Dad socks at a bus stop. But an entire industry is based on pretending that this is the case.

tl;dr

Facebook will try to sell you Football Dad socks even if you’re a 55 year old childfree woman who posted once about hating football, because that data is utterly useless.

Spies are probably reading your posts though, no matter how boring.

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If you don’t think you need to read this post because you’re always giving Good, Helpful Advice as a Good, Helpful Citizen, this one is for you. I’m sure you probably mean well, but it is with a heavy heart that I must inform you that you’ve likely annoyed the hell out of someone at some point or another. Probably more than once. Maybe it’s a regular pattern of behaviour. This post is for you.

And if you’ve ever been in that situation where all you wanted was to make a funny post on the internet about your cat drinking water from the toilet, to be deluged with links to cat fountains and lists of various germs that may or may not live in a toilet, this post is for you, too. Just leave the link there.

Here is a guide to reply menace behaviour, and how to just not be that menace.

Why is unsolicited advice so annoying?

At best, it is draining and exhausting. Most people just want to post in peace. Unless you’re on Quora or the specific questions-focused forums, the general function of social media and the internet is not asking for advice about every little thing. Maybe it’s just venting. Maybe it’s saying something funny.

Remember Clippy from Microsoft Office? You’re just trying to write a letter, and this insufferable little paperclip is popping up constantly with his vapid googly eyes and awful eyebrows and that fucking condescending smirk and his horrid little bendy body and oh god the colour of that speech bubble, like slightly worrying vaginal discharge, and the “it looks like” why is it so passive aggressive why- Sorry, I lost myself there. In short, Clippy was an irritation, and you’re giving someone’s notifications tab the vibes of using Word in 1997, which nobody wants to go back to.

At worst, it makes someone feel stupid. This is particularly pronounced if you’ve decided to shower advice on neurodivergent people. It can dredge up memories of unhelpful teachers trying to “correct” you because they think you’re fucking thick. Thank you for telling me to use a ruler before underlining a title, Mrs Dobson, I never would have thought of that myself, for I am but a big ol’ dunce.

An advice reply can feel that way. It feels like you think I’m so dim-witted that it never would have occurred to me that you can buy harissa in a supermarket when I mentioned making a small batch of emergency harissa.

There is a gendered element to this, too. Mansplaining is something which most women on the internet have experienced fairly frequently. It is exhausting. It is patronising. It is the background hum of patriarchy.

You might not personally be mansplaining. Maybe you’re not even a man. But those who have been on the receiving end of mansplaining are sensitive to it. Your attempt to help can come across as mansplaining, and throw you straight into the draining and exhausting pile.

If you are offering advice to a woman, be aware of this context. Be extra rigorous in checking that your advice will be in any way helpful or welcome.

Anyway, that over, and a few weirdly specific grudges aired, let’s get on with a little checklist of Clippy behaviours, and how not to do them.

Is someone asking for advice?

Study this carefully:

?

This is a question mark. When it appears in a sentence, it means a person is asking a question. If it is not there, it means they are not asking a question. A question is an invitation for a response, and perhaps a request for advice. If someone’s asking a direct question, they are soliciting advice. In this situation, advice is probably welcome – although please work through the other points in this post to make sure you’re giving helpful advice.

When someone is not asking a question, they probably do not want advice. This means, you have not been invited to give it. Your advice is not welcome. No matter how much you think there’s a solution to their predicament or they could do things a little differently, you’ve not been invited to share your advice. So don’t.

Is your solution helpful now?

Sometimes it’s clear someone has a question. The question mark is in the post. Is your advice helpful to the current and specific situation that they are asking about? If it isn’t, then don’t bother. Here’s a few examples of relevant and irrelevant advice.

“I keep getting [specific error message] in Windows! Why is this happening?“

You might think to yourself that this person should be using Ubuntu, because you think Ubuntu is much better. However, that advice isn’t going to solve the immediate problem of the error message in Windows. Unless, of course, what you’re advising them to do is wipe their PC, download Ubuntu, create a bootable flash drive, boot it, install and configure all the settings, and obviously read all the documentation – all to resolve the specific error message they were asking about.

Argh! My gas supplier is ripping me off with estimated usage!

Gas heating is bad for the environment and expensive. There’s good solutions to ending our dependence on gas, such as loft insulation and heat pumps. That’s not what this person is asking. Getting insulation and a heat pump installed isn’t going to solve the immediate problem of the gas supplier assuming this person is heating their house to tropical levels and spending all day gently sous vide-ing themselves in a hot bath. Unless, of course, what you’re advising them to do is find £15,000 and a builder, check everything is legally compliant, fill out any relevant paperwork and undertake home renovation work – all because their gas company is ripping them off.

With this example, note that there is no question mark. So any intervention sincerely advocating for heat pumps is doubly unwelcome.

What you’re doing when wading in with advice such as this is expressing an opinion instead of giving helpful advice. You’re welcome to your opinions, but when someone is asking for specific advice, it is not the place to share your opinions. So just don’t.

Did you just google that?

Sometimes you don’t know the answer to a call for advice, but you know how to google it. So you do, and respond with “hey, I don’t know if you know this, but here’s an answer I found!” You send this person the very helpful advice that you found by searching for it.

Here’s the thing: you are not the first person to have thought to type this question into a search engine. The first person to do that would be the person asking random strangers on the internet for advice. If the answer to the question can be found within the first few pages of a search with your preferred engine, then it’s probably not the answer someone needs, because they’ll have checked for that too.

You don’t have anything useful to add, and that’s okay. Go about your day.

Is there a minor grammatical error that you want to correct?

Just don’t do that, Mrs Dobson. If you understood the meaning of a post which used “their” instead of “there”, then the meaning is clear and there’s no need to correct it. All you’re doing is making someone feel stupid and small for no reason whatsoever.

Has someone already answered the question?

A cool feature of the internet is you can see what other people have said about the post. A less cool feature of the internet is that outside of office suites, no website has the functionality of making a comment as “resolved”. This means you’re going to need to do thirty seconds of detective work to check if the question has already been answered. Scroll down from the post, and look for someone else saying what you were going to say. You might even find a comment from the original poster saying “I figured it out!” If it’s been said or solved, you don’t need to say anything!

Handling ambiguity

Sometimes things are not as clear-cut as I’ve made out. For example, I used question marks in the section headers, and I’m not asking for advice. Or sometimes, it can be unclear if there’s a request for help or just a vent. Here is something you should say to someone in these situations, before going ahead and offering advice:

“Would you like advice?”

If they answer “yes”, then go at. If they don’t reply, or say “no”, then this is a situation where your intervention is not welcome.

If you have something really relevant to contribute based on personal experience, in these situations – and these situations only – your reply may be welcome. Begin by saying that you have had the same experience. Then, acknowledge how that makes someone feel, because chances are someone is venting out of frustration, or simply sharing a funny anecdote. Finally, add something which you found personally helpful and implemented in your own life. For example:

“The exact same thing happened to me. My boyfriend was constantly correcting me on minor things, and it was the most annoying thing in the world. I dumped him and never looked back.”

In this example we see first an acknowledgment of personal experience. Then, an expression of sympathy and the feelings involved. Finally, some useful advice that you applied successfully.

One final treat, for those of us receiving unsolicited advice

Obviously, please feel free to share this in case of unsolicited replies. But sometimes, you’re probably not in the mood for dealing with this. Here is an image of Clippy with a speech bubble coming out of his mouth to make it look like he’s giving the advice if you reply to the reply with it.

I don’t know the provenance of the meme. I first encountered it in a tumblr post which has been sitting around in my screenshots folder for years and I can’t even find the original link because searching tumblr is a pain in the arse.

Thank you for reading thousands of words of unsolicited advice. Please implement it in your life.

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A brief tl;dr before we dive into this project that I set for myself before immediately descending into madness:

I used Twitter’s new system of displaying views on every tweet to compare a sample of Twitter Blue users to a sample of users who didn’t pay real money for a little badge. Those who aren’t paying for Twitter Blue get more views on their tweets, probably because they make good tweets rather than doing crypto scams.

In this post, I am going to be as detailed as possible in my methodology so you can replicate my work to address the potential flaws and biases, if you wish. You can also download my data set here if you’d like to reanalyse, or run more analyses on the data because there’s probably some more interesting things in there about the difference between normal Twitter users and the Blue weirdos, which I couldn’t be bothered to look at.

Introduction

Since buying Twitter, Elon Musk has coped hard with his divorce by making changes. This has included flooding the platform with Nazis, but more pertinent to this research project, introducing Twitter Blue, the special system for special big boys where you get a little blue tick, and you’re promised your posts will be boosted. He has also added a wee hit counter to every single tweet, which renders muscle memory of how to like or retweet entirely useless, and gives the entire app the look-and-feel of a Geocities website.

The view counter has been criticised as being fake, but for the purpose of this project that I set myself to, I am going to sincerely pretend it gives a true and accurate read of the number of people who have seen a tweet. It probably is somewhat inflated but I suspect it gives a reasonable ballpark.

Given the divorced saddo’s insistence that his magic medicine Twitter Blue will help increase views, I set out to use his view counter to address the research question:

Do Twitter Blue users receive more views on their tweets?

Method

Sampling Twitter accounts

By far the most challenging element of this task I had set myself was finding Twitter Blue users as the test group. The big problem here is I usually immediately block conspiracy theorists, Tesla fanboys, or crypto scams. However, my block list is long and lengthy, and would involve also wading through a bunch of transphobes and misogynists, so I had to use different methods.

I considered tweeting something mildly critical of Elon Musk to summon them to my mentions, but decided against this approach as it would skew the Twitter Blue sample into those who were feeling particularly lonely and in need of attention that afternoon. Plus, my account would probably get banned before I had gathered sufficient data. In the end, I looked at the replies under an Elon Musk tweet, and selected the first 20 Twitter Blue accounts that I saw replying. They may appear in a different order to you, because Twitter is an algorithmic hellscape.

I’d initially intended to use a sample size of 50 Twitter Blue users to give myself a nice big sample size, but I entirely lost the will to live at 20 because their accounts are so terrible to have to look at, so this analysis is based on 20 Twitter Blue users. I cannot emphasise enough how thoroughly depressing it was to look at even 20 accounts. I’ve never seen so many crypto advocates, cranks and NFT profile pictures at once.

The control group consists of 20 users with no blue ticks. These were identified by opening up my timeline and selecting the first 20 accounts whose tweets I saw. This included accounts retweeted into my timeline. I have my timeline set to chronological, if that helps you to replicate my methods.

“Legacy” blue ticks – the ones who earned their ticks by being notable – were excluded entirely from this analysis because I’m not sure if that does anything to visibility with the algorithm tweaks that Space Karen has been making.

Sampling tweets

To get a fair measure of views without skewing based on one tweet that did numbers, I used the 10 most recent timeline tweets that each of the 40 accounts included in the analysis had made. Replies were excluded from the analysis, as were retweets.

If an account did not have 10 tweets with the view counter visible, i.e. they had not tweeted sufficiently since the view counter had come into effect, they were excluded from the analysis. Four accounts were excluded from the Blue sample, and three from the control sample under these criteria. To keep the total number of included accounts in the analysis at 20, if an account was excluded, another account was selected using the criteria above.

View count metric

The view count as presented on each of the 10 tweets for each account was used, under the assumption that it was probably at least somewhat vaguely related to the number of times it had been seen.

A mean of all views of all of the 10 tweets per account was calculated.

Results

Demographics

Accounts were not an exact like-for-like comparison. The Twitter Blue accounts had more followers on average than the control sample; and the control sample had, on average, an older account.

	Twitter Blue	Control
Mean followers	14099.2 SD = 19526.2	5801.1 SD = 5632.4
Mean account age (months)	54.4 SD = 55.6	123 SD = 55.4

Tweet views

The mean views of tweets from Twitter Blue accounts was 841.4 (SD = 774.4). The mean views of tweets from the control accounts was 1875.4 (SD = 1780).

Now, that’s obviously a pretty big difference. Despite having less than half the amount of followers the control group had more than twice as many views per tweet. I decided to go just a little bit harder, though. An independent samples t-test was used to assess whether this difference was significant. It was: t(38) = 2.38, p = .02

There was a statistically significant difference in number of views on tweets: users who had not decided to pay for Twitter Blue received significantly more views.

Discussion

Twitter Blue accounts just aren’t receiving the views that organic accounts are, despite the algorithmic boosting that they are receiving. This is probably because they’re just making bad posts that nobody actually wants to see. They’re not even getting ratio’ed for their bad takes, because their bad takes are so terminally pedestrian. It is possible that the difference is so marked between the Twitter Blue accounts and the control group because I have exceptional taste in curating my timeline, but I really cannot emphasise enough how bad the Blue tweets were. It was an utter morass of crypto scams peppered with weird anti-vax conspiracy therories. Nobody wants to see that, not even other Twitter Blue subscribers.

Future directions

I have supplied by data set here. There’s a few interesting bits and bobs in there that I noticed while inputting it but didn’t bother analysing because it’s a small data set and also my girlfriend wanted to go to the pub, which was a better life choice than the one I’d made. If you have time on your hands, perhaps you’d like to have a go at addressing one of these questions:

1. Are there indicators of organic reach within the control sample? The numbers in general among the control group felt a little more indicative of consistent reach peppered occasionally with a tweet that did numbers. Maybe have a go at smoothing and seeing if that’s the case.
2. I didn’t adjust for age of the account or follower count. Does this make a difference? Try playing around with that.
3. If you really, really have a load of extra time and a high tolerance for seeing the most tedious posts on the internet, why not have a go at running this analysis with a larger sample size and sampling which is not curtailed by losing the will to live completely?

Thank you for reading this labour of spite. I cannot believe I did this to myself.

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Twitter is going one of two ways. Either Elon Musk is going to burn the entire thing down swiftly in a fit of pique, or it will go the way of Tumblr: become a wholly unprofitable, entirely unmarketable, good, honest shitposting platform. At the same time, whatever direction Meta is going in… doesn’t have legs.

Going down with it will be entire industries of marketing grifts. Those listening tools. Those automated tone detectors. The lucrative doohickeys for identifying microinfluencers. The five-year engagement strategies and the ebooks and the webinars, all of them entirely irrelevant. Elon Musk isn’t the only one who is going to lose a lot of money.

I posted this on Twitter, but since there’s a decent chance I won’t be able to repost my tweet when the time comes to declare I’m right, I’m going to say my prediction here:

We are about a year out from the marketing industry declaring the death of social media marketing.

It’s an industry entirely formed on lines going up on made-up metrics and those lines are going to go down. They could introspect and identify that perhaps their entire shtick was built on some things they made up and told each other and amplified. But no, it’s the children who are wrong.

Where the grift shifts, I don’t know. It’s true that their entire pseudoscientific theory base and measures and methods will collapse. So to them, social media marketing is dead. And they’ll have to create something else in its place.

But it isn’t, not really. It becomes a space where the individuals with their etsy shops can thrive, free from the colossal forces of the algorithm-gaming industries. And I think that’s the best outcome all round.

Also, we can all enjoy some good, honest shitposting.

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Last week, Lady Rona called my name. The lateral flow test lit up like a fucking Christmas tree in seconds. The confirmatory PCR came through positive the next morning.

I’m a high risk person. I’ve spent the last two years being fairly terrified of catching the virus because I’m aware it could be very bad for me. Even though I’m fully vaccinated and boostered, and got in early on that because of my high risk, I’ve still been, basically, fucking terrified of catching it. One of the first things I thought when the test came up positive was that I should start planning my funeral and pack a bag to take to the hospital.

I suppose this post is especially for people like me, who feel like I felt. It’s also for everyone. These are the things I learned from my brush with Miss Rona.

Sometimes mild really does mean mild

“Mild” as defined by governments is taken to mean cases which don’t need urgent medical attention. This can be a level of ill which is Pretty Fucking Sick, and much worse than a horrible flu. But for me, it really was mild. It was so mild I’m not sure I’d have noticed it had I not taken that test when I did.

I had two symptoms, if you can really call it symptoms. One was doing gigantic sneezes. Not even sneezing more frequently, possibly slightly above the average amount of sneezing I do at this time of year. They were huge sneezes though. Body-wracking intense. The world flying out of my nose intense. Whenever I sneezed into my elbow, it would be absolutely coated with sneeze like a nasal bukkake. That was pretty unpleasant. Also, for the first day or so, I constantly felt like I might sneeze at minimal provocation (I didn’t, usually).

The other one was a runny nose. A really, really runny nose that came in fits and starts, and when it was running I’d have to check the tissue to make sure it wasn’t a nosebleed. My snot was very watery, and any tissue it touched would immediately disintegrate. Most of the time my nose wasn’t running, and it wasn’t blocked up at all. It would just occasionally run like all fuck.

And that was it. It was annoying. It was also less bad than any cold I’ve ever had, and most of my seasonal allergies are nastier too. And the symptoms were gone after about three days.

Mitigations work

I credit my actually really really fucking mild case of coronavirus to public health measures. I received my booster in early November, which trained my body to throw any little spiky round boys out of my nose so quickly it was a bit too speedy for my liking. The public health measures in place also meant that even though I was sufficiently exposed to be infected, I didn’t receive a particularly high viral load. Research suggests the amount you’re exposed makes a difference to how sick you get, and I caught it at a time where masking indoors is mandatory. I’ve also been wearing an FFP2 mask out and about, offering me a greater degree of protection, and prefer to socialise outdoors. All of this helped me to have a coronavirus experience which was mostly lounging around receiving gifts, being waited on, and eating grapes like an indolent classical princeling.

I don’t know where the fuck I caught it

I initially suspected I’d picked up my rona from a trip to Borough Market, where it was pretty crowded, followed by a couple of pints in a beer garden which was also crowded and fairly covered. But nobody I was with that day picked it up. In fact, nobody I’d seen at all in the week or so preceding my positive test had got it – I advised literally everyone to check!

So I’ve narrowed it down to a couple of possibilities, having ruled out the more obvious suspects. I might have caught it off a coke can, but this seems unlikely as it would have entailed someone with absolutely filthy hands they’d just sneezed in giving that can a good rub down. I might have picked it up the one time I took public transport on my own, sitting in an empty train carriage for a 15 minute journey while wearing an FFP2 mask, but that seems unlikely too, on account of all of the mitigations. So my best working assumption is I caught it outside, specifically from an awful child on a scooter who coughed directly in my face.

Unfortunately, my partner and I had been operating on the assumption that we’d been exposed together, probably at that Borough Market trip, so we didn’t take any steps to avoid me giving it to her once my case was confirmed. She tested positive on Monday. She’s doing fine, she’s even less sniffly than I was. Still, oops.

The lateral flow tests actually work

I’ll admit it. I’ve been incredibly sceptical of the value of lateral flow tests. I was never sure if they were especially accurate, or if I was doing it right… until I tested positive.

The test I took last week turned red literally immediately. The test line appeared even before the control one. It was bright fucking red. It was so quick I assumed it had gone horribly wrong, so I took another. It did the exact same thing. I cannot emphasise enough how quick the reaction was. It was cartoonish.

Two days later, I took another test and I really fucking half arsed it, out of sheer curiosity to see if it would pick up anything. I poked the swab a little bit up my nose, gave a cursory little rub to each nostril and then swirled it in the liquid for a couple of seconds. It still showed a positive result as soon as the drops went in.

By day 5, when I could take my first test to get out of isolation, it was taking longer than mere heartbeats to show a positive result – I think it was about five minutes, and the line was fainter. Day 6, it was negative and today it was too, so I’m free as a bird and feeling a lot better about the sensitivity of lateral flows when you’re riddled with rona.

I didn’t kill my girlfriend’s dad

My other worst case scenario with covid was infecting someone else. And all right, I did, but my partner healthy and it’s sitting fine on her. The bigger worry was, the day before I tested positive, we’d gone for a drink with her dad. And he had taken a sip from my glass to try my beer. And I’d sneezed intensely a couple of times that day, so was probably already infected.

Was I about to have committed the crime of girlfriend’s-dad-icide?

Once again, it was fine. I am apparently not very good at infecting others with the coronavirus. He’s not even sniffing a little bit.

Nevertheless, I don’t think I could live with myself if I did manage to make someone seriously ill. Even though I’m officially allowed to leave the house, I’m being more meticulous than I had been about taking steps not to infect others. I am in a relationship with hand sanitiser. The big guns masks, even for just stepping into the Nisa to buy some crisps. My future plans are all very well ventilated and ideally outdoors, because I do not want to have that panic again.

Self isolating in a small flat sucks

Unfortunately, I live in London. This means I live somewhere incredibly poky (or, as an estate agent would put it, cosy). It was just about all right when I tested positive but my partner was negative and able to go out, do the shopping, take herself for a solo pint and all around give ourselves space.

It was not so good when she tested positive and we were on top of each other. We’ve been treating ourselves to taking long baths for some alone time. I couldn’t be more excited about the prospect of getting to take myself to a beer garden to sit on my own with a pint and book now I’m out of covid jail.

The regulations are shit. We all deserve better.

The equation has shifted and for all of us, even the most at risk, the risk of a really boring week is much higher than the risk of getting seriously ill. That’s thanks to mitigations and public health measures.

But the thing is, living with the virus doesn’t have to mean an endless parade of catching it and streaming snot everywhere. We just don’t have to do that. Something different is possible.

The risks of covid, in conjunction with other winter diseases, are still unacceptable, even if the vast majority of us – even the at-risk – will be just fine. And measures like masking and adequate ventilation don’t just reduce transmission of coronavirus. They also drastically reduce the other pesky bugs like flu. Wouldn’t it be great if flu season was much smaller?

And these last couple of years of the pandemic have demostrated just how we can do that. What we need is a cultural shift towards valuing disabled lives. A culture of consideration, being polite enough to wear a bit of paper over your face when you’re in crowded places or staying at home when you’ve got a cold. A culture where lives are valued over presenteeism

And more than ever we need to fucking dismantle capitalism. Lots of benefits to that. In terms of immediate public health measures, there’s moves the state could easily make as transitional demands: better building standards for ventilation, retrofitting schools, supporting people to be vaccinated, liveable sick pay and sanctions for employers who are not prioritising their workers’ safety.

They won’t do that. Which is why we have to demand it, and keep demanding it. It’s achievable with collective action.

Unfortunately for those in power, Lady Rona didn’t carry me away. And I’m damn well going to yell about how shit they are.

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I listened to the clip everyone is talking about: Jesse Thorn’s interview with Robert Webb, challenging him very mildly on transphobia. It’s a fascinatingly instructive as to how the mind of The British Strain Of Transphobe works. When presented with an incredibly mild refutation of his stance, there was one thing he was incapable of saying: “I was wrong and not in possession of all the facts.”

This is the crux of The British Strain Of Transphobe mindset. It’s a core part of their identity that they’re smarter than everyone else. The vast majority of them start life on the private/grammar school-Oxbridge pipeline, where in place of education they’re just told this. The possibility of being incorrect is something The British Strain Of Transphobe is incapable of processing, because they’ve spent their lives believing they’re cleverer than everyone else, and this belief is integral to their belief of who and what they are. The British Strain Of Transphobe lives within an echo chamber of similar people. This is why, for example, transphobia spread like wildfire among the sceptic community, where many organisers are posh white folk, and it hinges on the belief of being smarter than everyone else.

And so, how does the British Strain Of Transphobe react to someone raising the mere possibility that they might be incorrect about something? Badly, because they take it as a fundamental attack on their identity as a person who is smarter than everyone else.

In the Webb interview, you can hear his rising sense of defensiveness, of something dancing around anger. This is because he is a man who cannot process the concept that he might not know everything, because if he’s not smarter than everyone else, what even is he? The British Strain Of Transphobe, cosseted in their echo chambers, can, most of the time, ignore or dismiss the thing which frightens them most – not, in fact, being smarter than everyone else. They shut out the messengers who might point out they could be wrong about something. On social media, they can put it down to trolls. In their vanishingly tiny circles, they shut themselves away from anyone who might point out there’s something they don’t know.

But the Webb interview was different. In this instance, Webb couldn’t dismiss the source of the message. He’d just spent half an hour talking with a well-educated arts and culture host – someone he respected. And then – wham! – this person Webb considered an equal smacked him with the thing he feared most.

Essentially, what you are witnessing in the Webb interview is the man having an existential crisis.

The actual subject matter of what Webb was wrong about was irrelevant to him. The thing which rattled him was a concept he and other British Strain Of Transphobes structure their lives around avoiding entertaining: that he was wrong about something.

Now, it’s unfortunate that despite having an understanding of the problem, I have no suggestions as to how to solve it. It’s just too powerful a part of their identity to challenge, someone living their whole life thinking they’re very smart and cannot be wrong. And when they are wrong, a polite (or impolite) refutation, “you’re wrong, here’s the facts”, just isn’t going to cut the mustard, because it’s not about the facts at all, it’s about their sense of self as a person who is smarter than everyone else.

At the end of the day, I don’t know what to do with this information. Maybe someone smarter than me can figure it out.

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