## Zoom in, zoom out

It was early on in the lockdown that the daughter participated in her first ever Zoom videoconference. It was an extended family call, with some 25 people across 9 or 10 households.

It was chaotic, to say the least. Family call meant there was no “moderation” of the sort you see in work calls (“mute yourself unless you’re speaking”, etc.). Each location had an entire family, so apart from talking on the call (which was chaotic with so many people anyways), people started talking among themselves. And that made it all the more chaotic.

Soon the daughter was shouting that it was getting too loud, and turned my computer volume down to the minimum (she’s figured out most of my computer controls in the last 2 months). After that, she lost interest and ran away.

A couple of weeks later, the wife was on a zoom call with a big group of her friends, and asked the daughter if she wanted to join. “I hate zoom, it’s too loud”, the daughter exclaimed and ran away.

Since then she has taken part in a couple of zoom calls, organised by her school. She sat with me once when I chatted with a (not very large) group of school friends. But I don’t think she particularly enjoys Zoom, or large video calls. And you need to remember that she is a “video call native“.

The early days of the lockdown were ripe times for people to turn into gurus, and make predictions with the hope that nobody would ever remember them in case they didn’t come through (I indulged in some of this as well). One that made the rounds was that group video calling would become much more popular and even replace group meetings (especially in the immediate aftermath of the pandemic).

I’m not so sure. While the rise of video calling has indeed given me an excuse to catch up “visually” with friends I haven’t seen in ages, I don’t see that much value from group video calls, after having participated in a few. The main problem is that there can, at a time, be only one channel of communication.

A few years back I’d written about the “anti two pizza rule” for organising parties, where I said that if you have a party, you should either have five or fewer guests, or ten or more (or something of the sort). The idea was that five or fewer can indeed have one coherent conversation without anyone being left out. Ten or more means the group naturally splits into multiple smaller groups, with each smaller group able to have conversations that add value to them.

In between (6-9 people) means it gets awkward – the group is too small to split, and too large to have one coherent conversation, and that makes for a bad party.

Now take that online. Because we have only one audio channel, there can only be one conversation for the entire group. This means that for a group of 10 or above, any “cross talk” needs to be necessarily broadcast, and that interferes with the main conversation of the group. So however large the group size of the online conversation, you can’t split the group. And the anti two pizza rule becomes “anti greater than or equal to two pizza rule”.

In other words, for an effective online conversation, you need to have four (or at max five) participants. Else you can risk the group getting unwieldy, some participants feeling left out or bored, or so much cross talk that nobody gets anything out of it.

So Zoom (or any other video chat app) is not going to replace any of our regular in-person communication media. It might to a small extent in the immediate wake of the pandemic, when people are afraid to meet large groups, but it will die out after that. OK, that is one more prediction from my side.

In related news, I swore off lecturing in Webinars some five years ago. Found it really stressful to lecture without the ability to look into the eyes of the “students”. I wonder if teachers worldwide who are being forced to lecture online because of the shut schools feel the way I do.

## Yet another social media sabbatical

Those of you who know me well know that I keep taking these social media sabbaticals. Once in a while I decide that I’m spending too much time on these platforms, wasting both time and mental energy, and log off. Time has come for yet another such break.

I had a bumper day on twitter yesterday. I wrote this one tweet storm that went viral. Some 2000 plus retweets and all that. Basically I used some 15 tweets to explain Bayes’s Theorem, a concept that most people find really hard to understand.

For the last 24 hours, my twitter mentions have been a mess. I’ve tried various things – applying filters, switching from the native app to tweetdeck, etc. but I find that I keep checking my mentions for that dopamine rush that comes out of new followers (I have some 1500 new followers after the tweetstorm, including Chris Arnade of Dignity fame), new retweets and new likes.

And the dopamine rush is frequently killed by hate, as a tweetstorm like this will inevitably generate. I did another tweetstorm this morning detailing this hate – it has to do with the “two Overton Windows” post I’d written a couple of weeks ago.

People are so deranged that even a maths tweetstorm (like the one at the beginning of this post) can be made political, and you see people go on and on.

In fact, there is this other piece I had written (for Mint, back in 2015) that again uses Bayes’s Theorem to explain online flamewars. Five years down, everything I wrote is true.

It is futile to engage with most people on Twitter, especially when they take their political selves too seriously. It can be exhausting, and 27 hours after I wrote that tweetstorm I’m completely exhausted.

So yeah this is not a social media sabbatical like my previous ones where I logged off all media. As things stand I’m only off Twitter (I’ve taken mitigating steps on other platforms to protect my blood pressure and serotonin).

Then again, those of you who know me well know that when I’m off twitter I’ll be writing more here. You can continue to expect that. I hope to be more productive here, and in my work (I’m swamped with work this lockdown) as well.

I continue to be available on WhatsApp, and Telegram, and email. Those of you who have my email or number can reach me in one of those places. For everything else, there’s the “contact” tab on this blog.

See you more regularly here in the coming days!

## Simulating Covid-19 Scenarios

I must warn that this is a super long post. Also I wonder if I should put this on medium in order to get more footage.

Most models of disease spread use what is known as a “SIR” framework. This Numberphile video gives a good primer into this framework.

The problem with the framework is that it’s too simplistic. It depends primarily on one parameter “R0”, which is the average number of people that each infected patient infects. When R0 is high, each patient infects a number of other people, and the disease spreads fast. With a low R0, the disease spreads slow. It was the SIR model that was used to produce all those “flatten the curve” pictures that we were bombarded with a week or two back.

There is a second parameter as well – the recovery or removal rate. Some diseases are so lethal that they have a high removal rate (eg. Ebola), and this puts a natural limit on how much the disease can spread, since infected people die before they can infect too many people.

In any case, such modelling is great for academic studies, and post-facto analyses where R0 can be estimated. As we are currently in the middle of an epidemic, this kind of simplistic modelling can’t take us far. Nobody has a clue yet on what the R0 for covid-19 is. Nobody knows what proportion of total cases are asymptomatic. Nobody knows the mortality rate.

And things are changing well-at-a-faster-rate. Governments are imposing distancing of various forms. First offices were shut down. Then shops were shut down. Now everything is shut down, and many of us have been asked to step out “only to get necessities”. And in such dynamic and fast-changing environments, a simplistic model such as the SIR can only take us so far, and uncertainty in estimating R0 means it can be pretty much useless as well.

In this context, I thought I’ll simulate a few real-life situations, and try to model the spread of the disease in these situations. This can give us an insight into what kind of services are more dangerous than others, and how we could potentially “get back to life” after going through an initial period of lockdown.

The basic assumption I’ve made is that the longer you spend with an infected person, the greater the chance of getting infected yourself. This is not an unreasonable assumption because the spread happens through activities such as sneezing, touching, inadvertently dropping droplets of your saliva on to the other person, and so on, each of which is more likely the longer the time you spend with someone.

Some basic modelling revealed that this can be modelled as a sort of negative exponential curve that looks like this.

$p = 1 - e^{-\lambda T}$

T is the number of hours you spend with the other person. $\lambda$ is a parameter of transmission – the higher it is, the more likely the disease with transmit (holding the amount of time spent together constant).

The function looks like this:

We have no clue what $\lambda$ is, but I’ll make an educated guess based on some limited data I’ve seen. I’ll take a conservative estimate and say that if an uninfected person spends 24 hours with an infected person, the former has a 50% chance of getting the disease from the latter.

This gives the value of $\lambda$ to be 0.02888 per hour. We will now use this to model various scenarios.

1. #### Delivery

This is the simplest model I built. There is one shop, and N customers.  Customers come one at a time and spend a fixed amount of time (1 or 2 or 5 minutes) at the shop, which has one shopkeeper. Initially, a proportion $p$ of the population is infected, and we assume that the shopkeeper is uninfected.

And then we model the transmission – based on our $\lambda = 0.02888$, for a two minute interaction, the probability of transmission is $1 - e^{-\lambda T} = 1 - e^{-\frac{0.02888 * 2}{60}} ~= 0.1$%.

In hindsight, I realised that this kind of a set up better describes “delivery” than a shop. With a 0.1% probability the delivery person gets infected from an infected customer during a delivery. With the same probability an infected delivery person infects a customer. The only way the disease can spread through this “shop” is for the shopkeeper / delivery person to be uninfected.

How does it play out? I simulated 10000 paths where one guy delivers to 1000 homes (maybe over the course of a week? that doesn’t matter as long as the overall infected rate in the population otherwise is constant), and spends exactly two minutes at each delivery, which is made to a single person. Let’s take a few cases, with different base cases of incidence of the disease – 0.1%, 0.2%, 0.5%, 1%, 2%, 5%, 10%, 20% and 50%.

The number of NEW people infected in each case is graphed here (we don’t care how many got the disease otherwise. We’re modelling how many got it from our “shop”). The  right side graph excludes the case of zero new infections, just to show you the scale of the problem.

Notice this – even when 50% of the population is infected, as long as the shopkeeper or delivery person is not initially infected, the chances of additional infections through 2-minute delivery are MINUSCULE. A strong case for policy-makers to enable delivery of all kinds, essential or inessential.

#### 2. SHOP

Now, let’s complicate matters a little bit. Instead of a delivery person going to each home, let’s assume a shop. Multiple people can be in the shop at the same time, and there can be more than one shopkeeper.

Let’s use the assumptions of standard queueing theory, and assume that the inter-arrival time for customers is guided by an Exponential distribution, and the time they spend in the shop is also guided by an Exponential distribution.

At the time when customers are in the shop, any infected customer (or shopkeeper) inside can infect any other customer or shopkeeper. So if you spend 2 minutes in a shop where there is 1 infected person, our calculation above tells us that you have a 0.1% chance of being infected yourself. If there are 10 infected people in the shop and you spend 2 minutes there, this is akin to spending 20 minutes with one infected person, and you have a 1% chance of getting infected.

Let’s consider two or three scenarios here. First is the “normal” case where one customer arrives every 5 minutes, and each customer spends 10 minutes in the shop (note that the shop can “serve” multiple customers simultaneously, so the queue doesn’t blow up here). Again let’s take a total of 1000 customers (assume a 24/7 open shop), and one shopkeeper.

Notice that there is significant transmission of infection here, even though we started with 5% of the population being infected. On average, another 3% of the population gets infected! Open supermarkets with usual crowd can result in significant transmission.

Does keeping the shop open with some sort of social distancing (let’s see only one-fourth as many people arrive) work? So people arrive with an average gap of 20 minutes, and still spend 10 minutes in the shop. There are still 10 shopkeepers. What does it look like when we start with 5% of the people being infected?

The graph is pretty much identical so I’m not bothering to put that here!

#### 3. Office

This scenario simulates for N people who are working together for a certain number of hours. We assume that exactly one person is infected at the beginning of the meeting. We also assume that once a person is infected, she can start infecting others in the very next minute (with our transmission probability).

How does the infection grow in this case? This is an easier simulation than the earlier one so we can run 10000 Monte Carlo paths. Let’s say we have a “meeting” with 40 people (could just be 40 people working in a small room) which lasts 4 hours. If we start with one infected person, this is how the number of infected grows over the 4 hours.

The spread is massive! When you have a large bunch of people in a small closed space over a significant period of time, the infection spreads rapidly among them. Even if you take a 10 person meeting over an hour, one infected person at the start can result in an average of 0.3 other people being infected by the end of the meeting.

10 persons meeting over 8 hours (a small office) with one initially infected means 3.5 others (on average) being infected by the end of the day.

Offices are dangerous places for the infection to spread. Even after the lockdown is lifted, some sort of work from home regulations need to be in place until the infection has been fully brought under control.

#### 4. Conferences

This is another form of “meeting”, except that at each point in time, people don’t engage with the whole room, but only a handful of others. These groups form at random, changing every minute, and infection can spread only within a particular group.

Let’s take a 100 person conference with 1 initially infected person. Let’s assume it lasts 8 hours. Depending upon how many people come together at a time, the spread of the infection rapidly changes, as can be seen in the graph below.

If people talk two at a time, there’s a 63% probability that the infection doesn’t spread at all. If they talk 5 at a time, this probability is cut by half. And if people congregate 10 at a time, there’s only a 11% chance that by the end of the day the infection HASN’T propagated!

One takeaway from this is that even once offices start functioning, they need to impose social distancing measures (until the virus has been completely wiped out). All large-ish meetings by video conference. A certain proportion of workers working from home by rotation.

And I wonder what will happen to the conferences.

I’ve put my (unedited) code here. Feel free to use and play around.

Finally, you might wonder why I’ve made so many Monte Carlo Simulations. Well, as the great Matt Levine had himself said, that’s my secret sauce!

## War, Terror and Leaderless Protests

A while back, I’d written on this blog that the phrase “war on terror” is incorrect since terrorism is not a war (actually I have written two posts on this topic. Here is the second one). A war is a staged human conflict with the aim being a political victory, and wars inevitably end in a political settlement, which in chess terms can be described as “resignation, rather than check mate”.

The issue with terrorism is that it is usually a distributed method. There is no one leader of terror. You might identify one leader and neutralise him, but that is no guarantee that the protests are going to end, since the rest of the “terrorist organisation” (a bit of an oxymoron) will keep the terror going. With a distributed organisation like a terrorist outfit, political settlements are impossible (who do you really settle with), and so the terrorism continues and there is no “victory”.

It is similar with spontaneous leaderless protests that have become the hallmark of the last decade, from Tunisia and Egypt in 2011 to Occupy Wall Street to the recent anti-CAA protests in India. To take a stark example with two protests based in Delhi, the Anna Hazare protest in 2011 was finished in fairly quick order (it started two days after India won the World Cup, and finished two days before the IPL was about to begin), while the Shaheen Bagh protests against the Citizenship Amendment Act have been going on for nearly three months now.

The difference between these two Delhi protests is that the first one (2011) had a designated leader (Anna Hazare, and maybe even Arvind Kejriwal or Kiran Bedi). And the protestors effectively followed the leader. And so when the government of the day negotiated a settlement with the leader, the protest effectively got “called off” and ended abruptly.

The Shaheen Bagh protests don’t have a designated leader to negotiate with (at least there are no obvious leaders). The government might try to negotiate with or round up or be violent to a handful of people who it thinks are the leaders, but the nature of the protest means that this is unlikely to have much effect since the rest of the “decentralised organisation” will go on.

In that sense, protests by “decentralised groups” are attritional battles where no negotiation is possible, and the only possible end is that the protestors either get bored or decide that the protest is pointless (that’s pretty much what happened with Occupy). Each member of the protesting group takes an independent decision each day (or night) whether to join the protest or not, and the protest will die down over a period of time (how long it will take depends on the size of the universe of people participating in the protest, overall interest level in the protest and how networked the protest is).

From that point of view, a leadered protest (like the Anna Hazare protest) can end suddenly (so everyone can go watch the IPL). A leaderless protest dies slowly and gradually (stronger network effects among the protestors can actually mean that the protest can die a bit faster, but still gradually).

There are claims on social media and WhatsApp groups that the communal violence in Delhi on Monday and Tuesday was designed in part to intimidate the Shaheen Bagh protestors to stop the protests. Even the violence was “successful” in achieving this objective, the leaderless nature of the protest will mean that it will only end “gradually”, more like a “halal process” rather than with a “jhatka”.

## WhatsApp Profiles and Wandering Spirits

As the more perceptive of you might know, the wife runs this matrimonial advisory business. As a way of developing her business, she also accepts profiles from people looking to get married, and matches them with her clients in case she thinks there is a match.

So her aunts, aunts of aunts, aunts’ friends, aunts’ nieces’s friends, and aunt’s friends’ friends’ friends keep sending her profiles of people looking to get married. The usual means of communication for all this is WhatsApp.

The trigger for this post was this one profile she received via WhatsApp. Quickly, her marriage broking instincts decreed that this girl is going to be a good match for one of her clients. And she instantly decided to set them up. The girl’s profile was quickly forwarded (via WhatsApp) to the client boy, who quickly approved of her. All that remained to set them up was the small matter of contacting the girl and seeking her approval.

And that’s proving to be easier said than done. For while it has been established that the girl’s profile is legitimate, she has been incredibly hard to track down. The first point of contact was the aunt who had forwarded her profile. She redirected to another uncle. That uncle got contacted, and after asking a zillion questions of who the prospective boy is, and how much he earns, and what sub-sub-caste he belongs to, he directed my wife to yet another uncle. “It’s his daughter only”, the first uncle said.

So the wife contacted this yet another uncle, who interrogated more throughly, and said that the girl is not his daughter but his niece. As things stand now, he is supposed to “get back” with the girl’s contact details.

As the wife was regaling me with her sob stories of this failed match last night, I couldn’t help but observe that these matrimonial profiles that “float around” on WhatsApp are similar to “pretas”, wandering spirits of the dead (according to Hindu tradition), who wander around and haunt people around them.

The received wisdom when it comes to people who are dead is that you need to give them a decent cremation and then do the required set of rituals so that the preta gets turned into a piNDa and only visits once a year in the form of a crow. In the absence of performance of such rituals, the preta remains a preta and will return to haunt you.

The problem with floating around profiles on WhatsApp, rather than decently using a matrimonial app (such as Tinder), is that there is no “expiry” or “decent cremation”. Even once the person in question has gotten taken, there is nothing preventing the network from pulling down the profile and marking it as taken. It takes significant effort to purge the profile from the network.

Sometimes it amazes me that people can be so nonchalant about privacy and float their profiles (a sort of combination of Facebook and Twitter profiles) on WhatsApp, where you don’t know where they’ll end up. And then there is this “expiry problem”.

WhatsApp is soon going to turn us all into pretas. PiNDa only!

From the time it launched in 2005 until 2012 or so when it was folded into Hangouts, I pretty much lived my life on Google Talk. The standard operating procedure when I came home from work (back then, I used to have jobs) was to open up my computer, open Google Talk and then ping 5-6 people who were online then.

The beauty of Google Talk was that you know who exactly was online at what point in time. So when you sprayed around your “hey how are you”s, you could target them at people who you knew were (or at least appeared to be) available to chat. This meant this had a high hit rate, and you could have productive conversations.

The problem with other chat mechanisms such as WhatsApp is that there is no “online” and “offline” mode. For example, if i’m working and I’m getting stuck and could use a quick conversation, turning my status to “available” on a chat room can then be a magnet for people to ping me. Or I can see which of my friends is online using their statuses, and then ping them.

With WhatsApp, I need to guess who might be available for conversation at this point in time. And that means lots of messages sent out which get responded to at the most inopportune of times (when I’ve got back my flow of work thought, for example).

In yesterday’s business standard I read about this app (whose name I now forget about ) which is trying to restart this kind of chats, signalling “availability” and chat rooms. Hopefully something like that will take off!

## Instagram targeting

Instagram is really good at what I call “one dimensional psychographic targeting”.

Essentially, based on the photos and videos (more likely hashtags) that you see, spend time on, like and comment, the platform figures out some of your interests and targets at you advertisements of products that serve these interests. And instagram manages to combine this with demographic information (where you live, etc.) to target advertisements better at you.

For example, of late I’ve been looking at a lot of weightlifting stuff on Instagram – I follow most of the coaches at my gym, and a few other handles that post fitness stuff. I’ve even posted a video of myself deadlifting.

As a result, Instagram has been following me with advertisements related to fitness, and the combination with demographics means I’m being served stuff I can get in Bangalore. For example, last two days I’ve been seeing ads of my own gym (!!). There are ads for whey proteins and healthy foods of all kinds as well.

This targeting is not perfect – for the last few months, ever since I returned to India, I’ve been bombarded on Instagram with advertisements asking me to emigrate to Canada (I don’t know what makes it think I want to move abroad again given I’ve just moved back home). The seemingly un-targeted mattress advertisements are everywhere. The shirt advertisements as well (though recently I uploaded a picture of my wardrobe on Instagram).

Nevertheless, this is a massive step up from what marketers were able to do a generation ago, where they could at best target based on a demographic. Marketers might have created elaborate psychographic or behavioural profiles of their target audiences, but when it came to advertising, the media available (newspaper, television and outdoors) meant that they had to collapse it into a demographic profile.

Instagram is not perfect, though. To the best of my knowledge, it can only target me on one “psychographic dimension” (“interested in weight lifting”, “interested in coloured chinos”, “likes Bangalore”) along with a multitude of demographic dimensions (I’m sure it’s figured out my gender, age group and maybe even caste, even if it exists in some vector somewhere and no human knows these classifications).

However, when you have created elaborate psychographic profiles, collapsing them into one dimension is still a simplification process. And so you get a reasonable degree of error in targeting. So I’m wondering what can be done that can enable advertisers to target me with more specific products that I might be interested in.

Finally, really how much are the likes of Charles Tyrwhitt, and some mattress brand whose name I don’t recall, willing to pay for their campaigns, given that their untargeted campaigns have beaten the highly targeted campaigns of the fitness guys and coffee companies to reach my eyeballs?

Two months back I completely went off social media. I deleted the instagram app from my phone and logged out of Instagram, Twitter, LinkedIn and Facebook on my computer. I needed a detox. And I found myself far more focussed and happier after I did that. And I started writing more here.

My first month off social media was strict. No social media under any circumstance. This was necessary to get rid of the addiction. Then, since I came back from the Maldives trip, I’ve been logging into various social media accounts on and off (about once a day on average) just to see if there are any messages and to browse a bit.

I only do it from my computer, and at a time when I’m not fully working. And as soon as the session is over I make sure I log out immediately. So the instinctive adrenaline-seeking opening of social media tabs is met by a login screen, which is friction, and I close the tab. So far so good.

In my infrequent returns to social media I’ve found that the most “harmless” are LinkedIn and Facebook (it might help that I don’t follow anyone on the latter, and if I want to check out what’s happening in someone’s life I need to explicitly go to their profile rather than them appearing on my timeline). LinkedIn is inane. Two or three posts will tell you it’s a waste of time, and I quickly log out. Facebook is again nothing spectacular.

Twitter is occasionally interesting, and I end up scrolling for a fair bit. For the most part I’m looking for interesting articles rather than look at twitter arguments and fights. I’m convinced  that twitter statements and arguments don’t add much value – they’re most likely ill thought out. Instead a link to a longer form piece leads me to better fleshed out arguments, whether I like it or not.

Mostly after a little bit of twitter scrolling, I find enough pieces of outrage, or news/political stuff that I get tired and log out. It’s only when I really need an adrenaline rush and don’t mind people cribbing that I stay on twitter for a bit of a long time (over five minutes).

Instagram, on the other hand, is like smoking cigarettes. When I smoked my first cigarette in 2004 I felt weak in the knees and a sort of high. It was in my final year of college, so I’d had enough friends tell me that cigarette smoking is addictive. And my first cigarette told my why exactly it was addictive.

So I made a policy decision at that moment that I’d limit myself to a total of one cigarette a year. I’ve probably averaged half a cigarette a year since then. My last one was in 2016.

Instagram is really addictive. It’s full of pictures, and if you avoid the really whiny accounts there is little negativity or politics. People make an effort to look nice, and take nice pictures, for instagram. So there is a lot of beauty in there. And if I choose to, especially when I’m logging in after a long time, I can keep at it for hours.

Instead I need to be conscious that it’s addictive (like my one cigarette a year rule), and pull myself away and force myself to log out. This also means that while I open twitter about once a day, Instagram is less than once a week.

I wonder what this means about the sustainability of social networks!

While discussing podcasts, a friend remarked last week that one of the best things about podcasts is the discovery of new hitherto unknown people.

In response I said that this was the function that blogs used to perform a decade ago. Back in the day, blogs were full of links, and to other blogs. Every blog hosted a column of “favourite” blogs. You could look up people’s livejournal friends pages. People left comments on each other’s blogs, along with links to their blogs.

So as you consumed interesting blog posts, you would naturally get linked to other interesting blogs, and discover new people (incidentally this was how my wife and I discovered each other, but that’s a story for another day).

Where blogs scored over today’s podcasts, however,  was that as they directed you to hitherto unknown people, they also pointed you to the precise place where you could consume more of their stuff – in the form of a blog link. So if you linked to this blog, a reader who landed up here could then discover more of me – well beyond whatever of me you featured on your blog along with your link.

And this is a missing link in the podcast – while podcast episodes have links to the guest’s work, it is not an easy organic process to go through to this link and start consuming the guest’s work (except I guess in terms of twitter accounts). Moreover, the podcast is an audio medium, so it’s not natural to go to the podcast page and click through to the links.

This is one of the tragedies of the decline of blogging (clearly I’m one of the holdouts of the blogging era, maybe because it’s served me so well). Organic discovery of new people and content is not as great as it used to be. Well, Twitter and retweets exist, but the short nature of the format is that it’s much harder to judge if someone is worth following there.

## Context switches and mental energy

Back in college, whenever I felt that my life needed to be “resurrected”, I used to start by cleaning up my room. Nowadays, like most other things in the world, this has moved to the virtual world as well. Since I can rely on the wife (:P) to keep my room “Pinky clean” all the time, resurrection of life nowadays begins with going off social media.

My latest resurrection started on Monday afternoon, when I logged off twitter and facebook and linkedin from all devices, and deleted the instagram app off my phone. My mind continues to wander, but one policy decision I’ve made is to both consume and contribute content only in the medium or long form.

Regular readers of this blog might notice that there’s consequently been a massive uptick of activity here – not spitting out little thoughts from time to time on twitter means that I consolidate them into more meaningful chunks and putting them here. What is interesting is that consumption of larger chunks of thought has also resulted in greater mindspace.

It’s simple – when you consume content in small chunks – tweets or instagram photos, for example, you need to switch contexts very often. One thought begins and ends with one tweet, and the next tweet is something completely different, necessitating a complete mental context switch. And, in hindsight, I think that is “expensive”.

While the constant stream of diverse thoughts is especially stimulating (and that is useful for someone like me who’s been diagnosed with ADHD), it comes with a huge mental cost of context switch. And that means less energy to do other things. It’s that simple, and I can’t believe I hadn’t thought of it so long!

I still continue to have my distractions (my ADHD mind won’t allow me to live without some). But they all happen to be longish content. There are a few blog posts (written by others) open in my browser window. My RSS feed reader is open on my browser for the first time since possibly my last twitter break. When in need of distraction, I read chunks of one of the articles that’s open (I read one article fully until I’ve finished it before moving on to the next). And then go back to my work.

While this provides me the necessary distraction, it also provides the distraction in one big chunk which doesn’t take away as much mental energy as reading twitter for the same amount of time would.

I’m thinking (though it may not be easy to implement) that once I finish this social media break, I’ll install apps on the iPad rather than having them on my phone or computer. Let’s see.