algo big data twitterbots



Ebola is a serious business, people are dying. The best way to stop the spread of a disease is to contain it at source. There are many organisations actively involved in treating people in West Africa. Do your bit Рlobby your politicians and shake them out of their apathy, or make a donation РI suggest the wonderful M̩decins Sans Fronti̬res as one such organisation worthy of your support.


Algobola is an investigation into social contagion.
Algobola infects Twitter, it is passed through the exchange of ‘social media fluids’ – in this case, the use of @ mentions. It’s an experiment to see how far a ‘social virus’ can travel, and whether its presence can have any effect on behaviour.
For the purposes of this experiment, I am patient zero – infectious to anyone I mention in my twitter feed (sorry friends). Once someone is exposed, they have a 50% chance of being infected. If they become infected, they are also contagious. There is a 30% chance of survival.
Changes in infectious status are sent directly to the affected user in the form of a modified avatar image.


Here’s a chart of some test data:


The number of infected people varies over time, depending on how promiscuous people are in their social network – to some extent it also reflects the day/night posting cycles of the infected population. This test had a 50/50 survival rate. The infection I’ve just started has only a 30% survival rate, so expect more death.
Infectious processes like this suffer from a computational explosion – within a few days, millions of people are affected. (Due to the limitations of Twitter’s rate limits, I can only monitor a few hundred people an hour, so the disease is going to be self-limiting.)
This work touches on two related ideas:
Firstly, it looks at how we respond to incurable diseases like Ebola.
In real terms, the experiment will infect a few thousand people – a drop in the ocean to 645,750,000 registered Twitter accounts. Indeed, this reflects the risk of contracting Ebola for those of us outside the currently infected areas. I’m sitting in Brighton, my chances of being exposed to Ebola at this point it time is effectively nil.
But our response to outbreaks like Ebola reflect who we are, as a collective humanity. It makes us question how far our empathy extends, and how we share our skills and resources in a time of crisis. The only sane response is treatment and containment at source.
However, human nature skews us towards conflating the risk of infection with the horror of actual disease. Because the disease is gruesome, horrific and arbitrary, we have a different kind of emotional response than we have towards real, but intangible threats, like global warming.


Secondly, it questions our apathy towards surveillance.
Algobola works across the network. The pattern of infection reflects social behaviours – it exposes who communicates with whom. This method of infection shares similarities with modern surveillance techniques. The number of ‘hops’ between you and a ‘person of interest’ can determine whether you are subject to further investigation, and can possibly result in real limits to your freedom.
Algobola explicitly exposes these kinds of connections, it shows how one random connection in your network may result in you being marked for ‘special attention’. Within a couple of hops the virus reaches thousands of people I’ve never met – when your government is ‘analysing your metadata’ the algorithms are working very much like a virus. Viruses are amoral, algorithms are much the same.
Will the introduction of this virus have any effect on Twitter behaviour? I’m not sure, I’m taking a baseline reading of how many mentions-per-day the user makes before and after the infection, so check back here for the results.


Here’s what happened