This text aims to rethink the different ways of approaching the social, the political and the urban, in an environment contextualized by the management of socio-technical data, as is the case of the social network Twitter.
Luis Fernando Gutiérrez Cano and Luis Jorge Orcasitas Pacheco
In this first installment, the analysis focuses on considering Twitter as a living network of behaviors and, subsequently, in a second installment, significant experiences of Twitter will be presented, as a real-world sensor, from the study of cases such as event detection, protests, sentiment analysis, health networks, among others.
This article is based on the study of the doctorate in Social Sciences of Ana María Miralles Castellanos, PhD, carried out at the Pontifical Bolivarian University, which is entitled The Public on Twitter as a Problem of Computational Social Science. The research of Dr. Miralles Castellanos is framed in the studies of Computer Science, Social Sciences and Complexity Sciences or Systems Theories, this, in the words of the researcher, in order to understand matters of public interest as an emerging resource on the web in the face of changes in the dimensions historically associated with the concept of the public (the collective, visible and open) that become connective, algorithmic visibility and open source.
But before starting with the development of this article, we must consider, as background, the original idea of Jack Dorsey, one of the co-founders of Twitter, when he proposed that this social media was: 1) like opening a tap of water; 2) a general service always available for the user to need; 3) immediate messaging from the question being asked; and 4) the beginning of interpersonal mass communication, in which an individual can make something public and millions can see it. It is explained that, in 2009, Twitter takes an important turn and changes its question to: what is happening?
The social from the post-disciplinary perspective
For the researcher Ana María Miralles Castellanos, today the study of the social in a network like Twitter must be approached from a post-disciplinary work, from which the knowledge that is produced by the collective interactions of those who interact with a socio-technical network such as Twitter can be obtained and that can be read from the construction of algorithms.
It should be added that, at present, humanity is immersed in a scenario marked by the complexity in the approach to social problems that, within an explanatory framework of the public such as Twitter, allows to understand matters of public interest as an emerging resource on the web, taking into account dimensions such as the connective, algorithmic visibility and open source.
For the researcher, today digital is more traceable and this is evident in the way algorithmically aspects such as attention are represented, which can be reflected when asking for the most seen, the most popular, the large numbers, the positions on the network, the person or the content that receives the most connections. Another term that the researcher contemplates is visibility, which is closely linked to attention, and that in networks is given by the degree of connection.
In algorithmic visibility, data space is critical because interactions are governed by those algorithms. The ways of knowing are altered, that is, space does not separate us by interactions because everything is within reach. Maximum presence is important if you want to be visible in a prestigious ranking. Most of the big players in the connectivity ecosystem present themselves as drivers of traffic.
Similarly, there is a technological pressure towards the selection of the most popular and the most connected. Hierarchical models that were known for marketing are reproduced in the network, but now it is to take advantage of it for the social. Twitter was born as a public service for expression, according to Miralles.
On the other hand, when referring to open source, reference is made to the need to maintain the web as a neutral space, as a space open to the logics of sharing with clear and visible rules, with the aim of taking advantage of the architecture of the platform, clearly defining the dynamics that favor the public interest and providing the data to the academy.
Another important aspect of Miralles' study has to do with Twitter's role in the connective. According to the researcher, the idea of connection in social networks is not an indicator of social relations in the strict sense, but of connections where interaction is not privileged but propagation. For the author, the propagation and influence in social networks determines them as a critical factor in the dissemination of information, that is, influence as a rational process that does not require appealing to emotions. Faster in the propagation of ideas. Retweets and mentions are representative of influence.
The social in times of networks and social media
Twitter has allowed the expansion of the public sphere and, as a network, has been participating in the creation of opinion formation and in the constitution of a deliberative democracy in which another mode of interaction of citizens is presented to impact the public. We are, according to Miralles (2017), in the era of connections, of the construction of the public in the deliberative and the connective, hence his approach to stylize the public sphere to talk about the public in the digital from the Science of Complexity, for a greater understanding of a phenomenon such as that of collective networks that, from an analysis to Twitter, it is envisioned as a dynamic, open organism, as a complex adaptive system from which useful data can be obtained.
Considering that the potential of social media from the study made by Dr. Miralles Castellanos allows the understanding of a new way of manifesting the social from the collective behavior that also admits the analysis of the data that are generated from socio-technical platforms such as Twitter, in the context of Artificial Intelligence as a form of representation of knowledge.
In this sense, the researcher analyzes the living network Twitter from its own collective dynamics that the social environment allows, such as algorithmic visibility and visibility, collective attention, propagation, bursts (peaks of activity), contagion, complex contagion, acceleration as a source of trending topics and the detection of topics as a network problem.
Digital is more traceable today
It is clear that for Miralles, mass and interpersonal communication have experienced processes of change when they go from being conceived in an analog way, from concepts such as the collective, the visible, the open, to move to a digital mode in which resources are the connective, algorithmic visibility and open source, presenting, according to the researcher, a shift in the following concepts:
It goes from mobilization to movement; from coordination to synchronization; from groups, to groups; from production to emergency; from media agendas, to the flow of data, from gatekeeping (who lets through the information that is known in public), to distributed curation (information is added); from opinion leaders, to superspreaders.
According to the above, these conceptual shifts cause phenomena such as the dispersion of attention to occur, in which collective attention emerges as one of the central issues in large-scale digital networks, called by the French as "socio-numerical networks". This means that the media are no longer as central as in previous communication models, in which they were filters of the public sphere and the public agenda, although they continue to act as important nodes for the distribution of opinions and information, in a complex network of content circulation.
The analysis of Twitter from Artificial Intelligence
For Miralles (2017) there are various research focuses of dynamics, from the prediction and correlation of events in relation to external sources, the analysis of specific user groups, the analysis of feelings, the analysis of tweet content, the analysis of propagation patterns, the arrest of events, the analysis of geolocation and influence studies. It is about thinking about the social in times of social media, all from the new dynamics that arise and that make it necessary to study them from new concepts, theories and methodologies different from those that have been applied since the eighteenth century. For the researcher, developing this new paradigm would allow us to understand the potential of analysis that Artificial Intelligence has in the identification from the visualization of social changes and from the connections that allow emerging phenomena to arise.
Consequently, the connections are no longer linear and this is evident when today the news comes out by any means and there are no controlling and dominant agents of the management of information. However, when extracting data from collective behaviors, big data must be taken into account as a primary factor, to identify in these data the topics that are spoken and thus develop semantic operations; likewise, to establish the collective dynamics, their visibility, their attention and to define semantic operations to bring together contentions and establish them in specific areas of dominance between the political and public opinion.
From polarized crowds to community clusters
To represent the semantic networks present on Twitter, the researcher assumes the proposal of Mapping of thematic networks of Twitter, with the purpose of explaining social behaviors in the network. This mapping was developed by the Pew Research Center, from the research of Marc A. Smith, Lee Rainie, Ben Shneiderman and Itai Himelboim.
This mapping proposes a cartography of conversational archetypes on Twitter, which are formed depending on the topic being discussed, the sources of information that are cited, the social networks of the people who talk about the topic and the leaders of the conversations, where each one has a different structure and social form: hub and divided spokes, unified, fragmented, grouped, and inward and outward. Here is a summary of this type of analysis.
1. Polarized Crowd: Polarized discussions present two large and dense groups that have little connection to each other, within the topics being discussed are highly heated politicians. It is seen, according to graph No. 2, that users depend on different sources of information: while liberals link to many traditional news sources, conservatives, meanwhile, link to a different set of sites on the web.
2. Tight Crowds: Discussions characterized by highly interconnected people with few participants talking about the same issue. As main topics are observed professionals, technicians, common tastes, among others, that attract their peers. These structures, as seen in Figure No. 3, show how networked learning communities work and how social media facilitates exchange and mutual support.
3. Brand Clusters: Discussion of popular, celebrity or brand topics. As shown in graph No. 4, many users are connected and connected to few centers. As an example: a great news item appears and the citizen looks there. There is a pattern of individual attention.
4. Community Clusters: Some popular topics develop multiple small groups that are often formed around a few centers. As shown in figure No. 5, each with its own audience, is made up of influential people and sources of information.
5. Broadcast Networks: Conversations that are triggered by large media outlets. Large news hubs in which comments on breaking news cause many people to repeat what prominent news and the media tweet.
6. Support Networks: Conversations about customer complaints, which are often handled by a Twitter service account that tries to resolve and manage customer issues around its products and services. This produces, as seen in graph No. 7, a cube and radius structure that is different from the diffusion network pattern.
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