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.