Abstract
Understanding the roles in the Relational Ecosystem, this chapter introduces a methodology according to which the nodes of the Relational Ecosystem can be classified according to their characteristics, and the ways in which it is possible to define further classification schemes to achieve different goals. The types of relations described in Chap. 6 are used to understand the ways in which they can be analyzed to classify the members of the ecosystem according to multiple schemes, to be able to provide meaningful insights about the ways in which data, information, knowledge, emotions, and opinions flow in the city: information brokers, hubs, experts, influencers, amplifiers, bridges, and more. This chapter describes how these and more roles can be identified for human and non-human members of the ecosystems (organizations, e.g., or objects, plants, environments connected through sensors, or even data and information sources of various kinds which may be present in the ecosystem).
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- 1.
This may seem as a paradox: How do I prevent data about a certain subject to enter the database, if I don’t store any information about that subject? How can I avoid data from or about Mr. X entering the database if I don’t store Mr. X’s name somewhere. There are techniques to do that in ethical ways. One possibility is to store Mr. X’s name in ways which are not accessible to us, so that we cannot know whose subject data we are filtering out. One way to do this is to act when Mr. X requests deletion of his/her data: When this happens, a cryptographic key is generated, used to store the information about Mr. X which we need to filter out his/her content from future harvesting processes, providing Mr. X with this key (so that, eventually and if he/she wants, they can use it to reverse the process, or to check whether everything worked out as expected) and to store it in a way which is unusable to us, and only accessible by the software (e.g., using hashing techniques with randomised keys, which only the software knows; another, more secure, way, is through the Ubiquitous Commons protocol, described at http://www.ubiquitouscommons.org/).
- 2.
The Human Ecosystems software platform uses and combines both approaches, for different purposes. Readers are encouraged to download the software, install it and use it, to practically learn the usages of these techniques, and the effectiveness of their results. To download Human Ecosystems refer to the notes in the previous chapters.
- 3.
Natural language analysis can be proficiently used for this purpose even in its simplest forms. Discourse analysis, for example, can be used in many cases by understanding the form and structure of textual exchanges, to understand whether they are questions, to which an answer is given: These tasks are performable automatically, by inspecting the grammatical structure of sentences, through their subjects, verbs, complements, punctuation, and other features.
- 4.
Created by bot auteur Darius Kazemi, Two Headlines works by taking headlines from Google News, and swapping out the key noun with a different trending topic. The best tweets read like reports from a world “where there is no discernible difference between corporations, nations, sports teams, brands, and celebrities”, Kazemi says. “It is generating jokes about the future: a very specific future dictated by what a Google algorithm believes is important about humans and our affairs.”
While being a peculiar example, it perfectly respects the influencer pattern we have just described, including the constant curation of sources and experts (although the “experts” in this case, are other non-human experts, like Google’s trending topics.
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Iaconesi, S., Persico, O. (2017). Understanding the Relations and the Flows of Emotions, Information, and Knowledge. In: Digital Urban Acupuncture. Springer, Cham. https://doi.org/10.1007/978-3-319-43403-2_8
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DOI: https://doi.org/10.1007/978-3-319-43403-2_8
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