Influence and causality: castration doesn't make you live longer

Hopefully obvious to most readers, but in my experience many people tend to still confuse the following two statements: "whenever A holds, B is more likely to hold" and "A causes B". They are not the same. The first statement expresses statistical dependence, the second causal influence.


Let me demonstrate this distinction with the following example: if you look at statistics, you may observe, that "people who do not have testicles live longer". Clearly, this doesn't imply that if you do have testicles then you should cut them off to live longer. (I mean, really, please don't try this at home). It simply reflects the fact that women tend not to have testicles and they also tend to live longer than men. Despite this extreme example - and several others - clearly demonstrating the possible implications of misinterpreting statistical dependence as causal influence, the distinction is very often overlooked not only by common people and journalists but, very sadly, even by scientists and policy-makers.


When analysing social influence of people, blogs and news sites, the distinction between causation and dependence is highly relevant: we may observe that whenever a particular user shares something in a social network, the volume of activity around the topic - on average - increases. But this alone does not imply that the user is actually directly responsible for this increase in activity, nor that she or he is influential.


Fortunately, in social networks, there are ways to explicitly record causal influence: for example, if Alice retweets Bob's message, or shares his post, it is very likely that there was direct causal relationship between Bob's activity and Alice's activity. But often such influences remain implicit in the data: instead of explicitly forwarding Alice's message, Bob may just post the same piece of information without explicitly acknowledging Alice as a source of inspiration. These situations make it very hard (although not impossible, that's my job) to disambiguate between alternative stories explaining the data: was Bob influenced by Alice, or is it just a coincidence that they both shared the same piece of information being influenced by third party sources.


The most powerful, although usually very costly, way of detecting causal influence is through intervention: to go back to our castration example, this amounts to cutting a few guys' testicles and implanting them into women and then measuring how long these patients lived. If you can do that - set the value of a few variables and observe what happens to the rest of the variables - you really are in a better position to detect causal relationships.


In a recently published study, Facebook's data team did just that in the context of social networks: they intervened. During their experiment in 2010, they randomly filtered users' news feed to see how likely they were to share certain content in situations when they do vs. when they do not see their friends related activities. Unsurprisingly from facebook, the scale of the experiment was humongous: it involved around 250 million users, 78 million shared URLs amounting to over 1 billion (as in thousand million, \(10^9\)) user-URL pairs. This randomised experiment allowed them to gain unprecedented qualitative insights into the dynamics of social influence: the effect of peer influence on the latency of sharing times; the effect of multiple friends sharing the same piece of information; connections between tie strength and influence. I encourage everyone interested in either causality or influence in social networks to look at the paper.


Finally, just to illustrate how hard inferring the presence of influence is, just consider this blog post: The underlying truth is that I first read about the Facebook study on TechCrunch, then I looked it up on Google News, chose to read the New Scientist coverage which finally pointed me to the facebook note and the paper. Now, had I not included this final paragraph, just imagine how hard it would've been to identify these sources of influence. Well, this is my (or rather, my algorithms') job.

Influentomics: the science of social influence

Coming up with new -omics has been a fast growing trend in modern science in the past decade. It’s not that hard: you simply start calling a field of science of your picking, i.e. what you, your close collaborators, friends and scientific-political allies are working on somethingomics. The collection of data, objects, concepts that pioneers of somethingomics study is then collectively called THE somethingome. 

The terms genomics, proteomics, transcriptiomics were the first ones I came across in my biolinformatics studies during undergraduate years. The term genome certainly is amongst the most widely adopted ones outside science, especially since the human genome project.

Like genomics, most -omics refer to a subfield of biology: connectomics studies patterns of connectivity between neurons in the brain; vaccinomics studies the genetic underpinning of vaccine development, etc. Take a look at the long list of omics on this website or at omics.org. Some omes and omics are even trademark names (I won’t cite them here for fear of misusing TM signs and being sued).

But omics’ have started appearing beyond the boundaries of biology: The legalome refers to the whole set of laws in a society. The Human Speechome Project looks at how children learn to speak, with the speechome being the collection of all the speech and speech-related interactions a child is exposed to since her birth. It’s now over a year since culturomics was born (see Science paper): culturomists look for patterns in the culturome: the vast amount of printed books mankind has produced - and Google then kindly digitised - since 1800. But my favourite -omics of all is arguably PhDcomics, which I follow more actively than any other.

So following the footsteps of great thinkers, I hereby declare my very own new pet omics, influentomics: the study of social influence. The influentome is the entirity of social interaction between groups of people and all social actions that can be used to detect or infer how members of a community influence each other’s actions and opinions. Some of this is available as observable data and can form the basis of analysis, a large part of it remains unobserved.

Today, the single most useful observed (or partially observed) subset of the influentome is the vast collection of social data about people on twitter, facebook and similar sites. These social platforms are to influentomics what high throughput microarrays were to transcriptomics: suddenly high volumes of fairly well organised data are available, opening the door for more sophisticated data-driven insights into influence than ever before. It is not only the scale and resolution of these datasets which is different. The limited range of social actions one can exercise on twitter or facebook also helps formulating theories: on twitter you can tweet, retweet, mention, use hashtags, follow, unfollow. On facebook you can become friends, now you can subscribe, like, post, repost, comment, become a fan, etc. All these are well-defined canonical social actions from which it is far easier to infer patterns of influence than on the basis of less structured data such as books, reviews and journal articles.

So it is no surprise we are seeing a surge of interest in mining the influentome; both in academy and in business. A month ago I joined PeerIndex, a fine example of young companies that try to leverage social data to provide measures of social influence. I’m looking forward to face the machine learning challenges that my newly declared field, influentomics, has to offer.