Evaluating WhatsApp groups

Over time I’ve come to become a member of several WhatsApp groups. Some of them are temporary, designed to simply coordinate on a particular one-off event. Others are more permanent, existing over a long term, but with no particular agenda.

Over this time I’ve also exited several WhatsApp groups, especially those that have gotten a bit annoying. I remember this day last year when I stepped in and out of a meeting, and I found a hundred messages on a family WhatsApp group, most of them being random forwards, and a few of them being over a page long. I quickly exited that group.

Not everyone quickly exits groups they don’t like, though. There is social pressure to remain, since anyone’s exit gets publicly broadcast in the group. Being a member of a WhatsApp group is the latest measure of conformity, and irrespective of how annoying some groups are, one is forced to endure.

Not all WhatsApp groups are annoying, though. Some groups I’m a member of are an absolute joy. There are times when I explicitly choose to initiate a conversation within the group, than bilaterally, so that others in the group can pitch in. And this taking of the conversation to the group is usually not minded by the intended counterparty as well.

Thinking about good and bad WhatsApp groups, I was wondering if there is a good and clean metric to determine how “good” or “useful” a WhatsApp group might be.┬áBased on my experience, I have one idea. Do let me know if you know a better way to characterise whether a WhatsApp group is going to be good or bad.

When you have a WhatsApp group with N people, you are essentially bringing together N * (N-1)/2 pairs of people. Now, some of these pairs might get along fantastically well. Other pairs might loath each other. And yet others are indifferent to each other.

My hypothesis is that the more the number of pairs in a group that like to talk to each other, the better the group functions (yes it’s a rather simple metric).

Now, this hypothesis is rather simplistic – for example, you can have threesomes of people whose mutual relationship is very different from that of any pair taken together. So this ignores a higher order correlation term, but improves simplicity. It’s like that benzene ring, where six carbon atoms bond together in a way no two of them as a pair can (forget the scientific term for such bonding)!

Yet, what we have here is a good measure of cohesion within the group. It also explains why sometimes the addition of a single member can lead to the destruction of the group – for it can increase the proportion of people who don’t like to talk to each other!

The model is incomplete, though. For now, it doesn’t differentiate between “don’t care conditions” (people in the group who are indifferent to each other) and “don’t get alongs”. If we can incorporate that without making the formula more complex, I think we might be up to something.

Maybe we should form a WhatsApp group to discuss what a good formula might look like!