How are relations structured and described in the Human Ecosystem?
In capturing data from social networks to describe the Relational Ecosystems of cities which we use for our visualisations and our analyses we are able to collect different types of data and information about people.
Note: it is important, here, to remember a few things
- we collect public data; the ones which people marked as public; the ones for which you don’t even need to log in to Twitter, Facebook, or other social networks to see;
- we make public all the data we capture and process, turning into Open Data sets, so that citizens can see and use it (as, currently, citizens are the only types of subjects which do not have access to this type of data: social network providers have it, companies and organisations can purchase it, the NSA has it, only citizens do not have access to this information)
- we constantly invite people to use this data in critical ways, trying to find positive uses for it, but also using it to understand the transformation of the boundaries between public and private spaces, our perception about what is private/public, their implications for our privacy, and to understand the counter-measures which we can take when we realise that something is wrong, too intrusive, and when we feel that we are subject to exploitation
We fundamentally collect these types of data and information about people, using the things which they publicly express on social networks, and turn it into an Open Data set:
- quantified data
- quantifiable data and categories
- topics/emotions networks
- relational networks
- emergent data
Let’s see what each data/information type is.
As the name suggests, quantified data is data you can count. Numbers, sums, averages, geographical coordinates and things like that.
Through social networks we are able to collect a number of data of this kind: how many times people interact with each other; how many friends/followers/relations they have; from which geographical location they write; how many times they discuss a certain topic; etc.
This type of data is often used to weight other data/information, or to perform quantitative analyses of certain phenomena, or even to guess the relevance and reliability of certain other information (for example, if a certain news item or information is constantly mentioned by members of a certain community which is relevant to the topic with a certain emotional expression could be more relevant than a single person mentioning it by chance).
Quantifiable Data and Categories
For example, if someone would mentions on a social network that he/she graduated from University with an Engineering degree, we could add them to the category of people with a university degree and tag them with “engineering” in our folksonomies. If, some times later, they would mention in a public message on social networks their interests for, let’s say, robotics, we could tag them with “robotics”, and so on, collecting information as we move on.
These first two types of data and information can be seen as a peculiar form of census. Peculiar in the sense that they are different from the data which can commonly found in, for example, national census, for a number of ways, for example:
- they are updated in near-real time (which good, but not always good: for example you may find yourself with a person whose information is updated yesterday, and one whose info is updated two years ago, because they left social networks; this could lead you to believe, referring to the last example, that you have two “robotics engineers”, and instead the least updated one became a pizza maker because he was tired of robotics);
- they are not certified (it happens that people do not tell “the truth” on social networks; or, better, people tell multiple version of truth and reality on social networks; this is dangerous for census, and it is, instead, highly desirable for anthropological, cultural and perception analysis, because it describes people in they ways in which they choose to represent themselves, their desires, fetishes and more).
On social networks people discuss multiple topics and express their emotions about them.
In Human Ecosystems this fact accounts for an enormous amount of information.
The topics which people discuss are captured as well as the ways in which they relate to each other. For example, if a person talks about Topic A and Topic B, it could point out that these topics are related in some ways, or that people talking about Topic A could be also interested in Topic B. Of course, these possibilities must be properly evaluated, and multiple types of evaluations of this kind are possible: the fact that this type of information is available opens up the possibility for these kinds of analyses to be performed.
The same can be said for emotions. Emotions are captured using Natural Language Analysis techniques, in relation to the topics about which they are expressed, in their sequence in time (how emotions evolve over time), in their connection to news and events (for example trying to understand if a certain news item might be connected to a transformation of the emotion about a certain topic for a person, or a community).
These, combined, form complex Topic/Emotion/People/Communities networks, which can be used to gain better understandings about the ways in which information, news, knowledge, emotions and opinions are created and flow across people, communities and cultures.
From what we have seen in the previous paragraphs, Relational Networks can originate in several different ways, and according to multiple modalities.
They can form by direct or indirect relation, or around topics, emotions, news, knowledge, information, expertise, and more.
A relation can form if I retweet a content, if I comment on a post, if I share some information, if I mention someone, if I talk about a topic that you’re interested in and in thousands of different ways, forming each person’s and organisation’s relational networks, and the ways in which they are interconnected, and in which they evolve in time.
Of course, not all relations are the same.
Relations have different weights, levels of persistence, verticalities, horizontalities, transversalities, liminality, among others:
- weight: the strength of a relation; a retweet is weaker than a conversation, for example;
- persistence: the duration of a relation; one casual message exchanged among two people is very different from a continuous state of conversation, across time, domains, interests;
- verticality: some relations are vertical, meaning that they are focused on a single topic/domain (or a limited set); for example they are the ones found in focus groups, or forums;
- horizontality: some relations are horizontal, meaning that they are not focused on a certain topic/domain, and they engage the same people across multiple topics; this is the case of friendship, for example, in which the people engage and possibly discuss a very diverse set of topics;
- transversality: these are relations in which both horizontal and vertical characteristics are manifested; sometimes they are vertical, sometimes they are horizontal; sometimes the two modalities originate one from the other (you start talking about something and end up talking about something else, in ways which do not permit deciding wether the relation is horizontal or vertical); this is the case which is used in social networks include different communities in a certain analysis, by finding convergence on a series of topics, and by researching the divergences and differences;
- liminality: these are liminal relations, on the border; they are relations which can connect or separate; they describe borders and transition spaces, interstices; they are specifically interesting for their characteristic of being across something, in-between, defining the ways in which something is connected or separated from something else, for example describing the bridges or separations across different communities or cultures.
All the types of data, information, knowledge and relation which we have analysed so far is, obviously, a representation of reality, from the multiple perspectives and points of view which can be present at the same time on such a thing.
This consideration leads us, in the Human Ecosystems, to also allow emergent data to, obviously, emerge.
In the idea of “finding the questions” before attempting to “finding the solutions”, we have created multiple ways in which emergent data and relations can be captured (the things we don’t yet know, the ones we’re not yet listening to: the “questions” which emerge from the ecosystem itself), and made it really easy to create additional logics and modalities according to which these kind process takes place.
We have always described this process as one of Senseability: the possibility to have different, additional, senses.
Using the modalities which have been described in the previous sections, it is possible to gain certain understandings about how people relate and interact, describing people’s roles in these interactions.
These descriptions, of course, have variations through time and contexts. People participate to different communities and cultures at the same time, with broader or tighter scopes, their roles within them changing all the time, as well as their level of engagement and the layouts and configuration of their participation.
While the idea of describing a static version of this scenario is impossible (and also useless and relatively dangerous, as it would mean to loose all the complexity and dynamics of the transforming human relations), some patterns constantly emerge in interesting ways.
For example, we have identified a number of behaviour/relation patterns which describe in simple, accessible ways people’s roles within and across communities.
Let’s analyse a few of them.
The Expert has many relations, often of the Vertical type, with multiple people and organizations referring to the Expert for information. The Expert’s outgoing communication and information (the arrows going out from the center) have a large Weight, they quickly establish influence and relevance for the people who receive them.
To understand the Hub, we must also take in consideration the dimension of time.
In the beginning, the Hub establishes many relations, interacting with a variety of subjects. Some of these connections have more weight than other, as the Hub takes them in more serious consideration (as can be seen, for example, by the fact that the Hub chooses to highlight some of them by sharing some of their information, by retweeting, commenting, re-posting, or other activities and processes). In this stage, the Hubs forms its network and its opinion.
In following stages, the Hub chooses to facilitate some connections (red arrows on the image on the right), putting subjects in contact. This, for example, can be performed by mentioning different subjects in the same message, or by proposing themes for common reflection to multiple people.
This is a cyclic behaviour, and repeats over time.
The Influencer has many, asymmetric, connections.
Many of these connections act as information providers: they are sources of news, information, knowledge, curiosities and more. The Influencer takes these information in serious consideration (thus their high weight). The Influencer accurately chooses its sources (for example Influencers usually follow only a few people on Twitter).
Occasionally, the Influencer combines the information coming from providers with the expertise provided by Experts (as described in the previous sections). This can be done, for example, by mentioning Experts in tweets and Facebook posts.
Whenever Experts provide feedback on these forms of engagement (the red arrows on the image on the left), the Influencers take these expression into high consideration (high weight), and often forward them to a wider audience (the blue arrows on the image on the right), provoking a wide discussion and, in general, influence (affect discussions, provoke transformation in opinions and emotions, etc.).
It is possible to see Influencers as specialised information and knowledge brokers: curating sources, selectively asking for expertise from experts, and disseminating the results. A one-man-publishing house.
The Amplifier is very similar to the Influencer, but with the Influencers replacing the Experts, as shown in the image. This provokes a radical transformation, in the fact that amplifiers provide a level of curation which is much lower (it is provided, after all, by the Influencers) and they “adopt” the opinions of selected Influencers and their providers of information and expertise, and make it a mission to amplify their network reach.
As the name implies, the Bridge is found in-between two or more contexts, which it interconnects. Formally, the Bridge sits in-between two networks (two communities or other relational networks of the various possible types, be them vertical, horizontal, transversal or else).
Bridges can be mono-directional (they have influence in one direction, but not in the other), bi-directional (they have influence in both directions), or a-directional (they effectively sit in the middle of two networks, but they do not have influence on any of the two sides).
Bridges are among the most important tools to create engagement and activation in wide communities: they represent (in practice, or in potential, as in the case of the a-directional bridges) the opportunity to interconnect communities and cultures.
For this reason, Bridges are among the most precious “tools” for Urban Acupuncture techniques, together with Influencers and Amplifiers.
Cultures and Communities
How do Communities and Cultures emerge from this type of analysis?
It’s a complex process, and one that is not static too. As already mentioned, cultures and communities change all the time, and they are not univocal in their description. Boundaries are fuzzy and blurry, and constantly change, as people discover the world and create relationships, forget about them, find affinities, establish conflicts, discover new hobbies and interests, develop passions and hatred, and more.
To gain a better understanding of how things work we can imagine the space of relations as a physical space, with forces that are different from the ones we are used to, but which work according to the same principles.
For example, let’s imagine that there are two “forces of gravity” in this space, which react to the various characteristics of the subjects in the Relational Ecosystem.
They are both “forces of attraction”, but work according to different logics.
The first is a Similitude Gravity, the second one is the Coexistence Gravity.
The Similitude Gravity reacts on how “things are the same”.
If we go back to the beginning of the post, to look at the type of data we collect about people, we will be able to see that a force of this kind could easily work for the two main types of data which we collect, the quantitative ones (and the quantifiable and categorizable ones) and the networked ones: for the first ones we can use a concept of direct similarity (how close are two numbers; if a content belongs to the same categories…); for the second ones we can use a concept of networked similarity (how a network of nodes of different types interconnected through links of different weights are similar to one another, for which there are a number of different algorithms present in technical literature).
The Coexixtence Gravity reacts on how “things can live together without conflict”.
Using this logic, the two types of information (quantitative and networked) will behave according to different schemes.
Quantities and categories will behave according to the logics of complementarity: small will attract large and vice-versa; A will attract B if B implies or complements A and vice-versa, etcetera.
Networks (topics/emotions, and relations) will behave according to the logics of compatibility: two people will attract each other if they express on a certain topic with similar or complementary emotions; and they will attract one another also if their relational networks are not in conflict with each other.
It is interesting to note that this logic can also be conveniently reversed to understand the conflicts in the Relational Ecosystem, which is as interesting and insightful as understanding the compatibilities. For example, in the logics of Urban Acupuncture understanding conflicts and differences is one of the major steps in resolving them and enabling different cultures, opinions, emotions and points of view to coexist in the same environment, providing richness instead of stress.
- according to Similarity Gravity, similar pieces of the puzzle attract each other;
- according to Coexistence Gravity, pieces of puzzles which go together attract each other.
Of course, one must not fall in the temptation of describing the Relational Ecosystems as a deterministic system.
Just as it happens for the physical universe, the universe of relations also has other forces which act at different scales and with different sensibilities, and also indeterministic forces. The forces described in this article, as all of the physics which we study in school, are usable, useful approximations of the complexity which happens in the “real” world (or even in the “plural real worlds”, when we start dealing with quantum physics and relativity). And as such they should be used.
In other scenarios, just like in physics, we could start exploring the Quantum Physics of Relations, or maybe a Theory of Relativity applied to Anthropology and Sociology, including principles of Uncertainty.
While it might seem far off, it is a very interesting area for exploration, for example starting from the Hawthorne Effect, or from the Rashomon Effect, or by the same observation of how ethnographers are not really “out of the system” which they observe and, thus, they contribute to the transformation of networks and relations.
And, in general, to direct research in an interesting direction, which is the one of inclusion: the elimination of the boundary distinguishing the observers from the observed, and suggesting an inclusive, participatory effort in the understanding of the dynamics of our ecosystem, to actively promote its well-being: a collaborative performance of co-existence.