Mathematical models inspired by ecology to understand social networks

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The ease with which anyone can create content online for free, especially on social media, has made information overload one of the defining characteristics of today’s communication systems. This has resulted in an increasingly intense competition for attention, which has become a scarce commodity. The researchers of the Complex Systems group (CoSIN3) of the Interdisciplinary Internet Institute of the UOC (IN3) María José Palazzi and Albert Solé – professor at the Faculty of Computer Science, Multimedia and Telecommunications? -, led by Javier Borge, participated in the design of a mathematical model inspired by ecology that allows to decompose and predict the patterns of interaction in a system as complex as the social network Twitter.

The model, published in the open access journal Nature communications, is fundamentally based on two variables: the mutualistic relationship (beneficial for both parties) between users and hashtags, and competition for visibility, reflecting the situation of natural ecosystems with limited resources. According to the authors, this ecological framework “offers a new and alternative way of understanding how Twitter works and can also be applied to other social media and communication ecosystems with similar characteristics.”

Researchers have looked at various phenomena that have gone viral over the past nine years. One of the events was the 2012 UEFA European Football Championship, from which they collected almost four million tweets from over 1.3 million users, who used almost 150,000 hashtags, June 19 to July 4, 2012. The UOC research team also studied Twitter. communication during the 2014 protests in Hong Kong. From these demos, they studied over 800,000 tweets from nearly 240,000 users, who wrote over 30,000 possible hashtags from September 27 to October 7. Another event they analyzed was the April 2015 earthquake in Nepal, taking into account nearly two million tweets from over 810,000 users and considering over 35,000 potential hashtags from May 8 to 14 of that time. year.

Parallel to the collaboration of flowers and pollinating insects

For decades, mathematical models have been applied to the fields of ecology and complex networks with the aim of describing the behavior of natural systems to predict aspects such as the evolution of species abundance. By observing the behavior of Twitter, the authors of the article identified similarities between some of these patterns and the characteristics of interactions on this social network. “Our intuition has told us that Twitter users, understood in the abstract, compete for a finite resource (attention) in the same way that pollinating insects like bees compete for nectar. Hashtags, words and memes also compete to be the most used, in a way similar to how plants use their scents and colors to passively compete for the attention of insects, ”the authors explained.

Specifically, the new study adapts the type of mathematical models used for more than 50 years in ecology to study natural mutualistic ecosystems (those in which species benefit from each other) to Twitter. “When a user chooses a hashtag, both agents benefit: the user because they believe that he adequately expresses their desires and by using it they will get more attention, and the hashtag passively because it will be broadcast to more users, thus reproducing the mutualistic relationship Our hypothesis was that if Twitter worked in a similar way to these ecosystems, we should be able to identify a certain match and be able to predict the patterns in which the social network is organized, ”explain- they.

Two models of behavior: state of rest and collective attention

The results show that on the basis of a minimum of ingredients (competition, mutual benefit and maximization of visibility), this model makes it possible to capture and predict what is really going on. According to the researchers, Twitter has two basic models: when attention is fragmented, the system is structured as “a modular network, that is, organized into different groups according to the interests of users around certain thematic hashtags” . But when there is an exceptional or viral event, which can be any extraordinary report, like an election, an earthquake or a TV show, “every user turns their attention to this phenomenon and the thematic communities disappear. , entering what we called the nested state. ” In these cases, the discussion revolves around a small group of users who generate and use a large number of hashtags adopted by virtually everyone in the network. Once interest in the event wanes, the system returns to its normal modular condition: the quiescent state.

One of the key aspects of this approach is that it is a simple model, given that with very few parameters it is able to capture the fundamental ingredients that drive emerging trends seen on Twitter and, moreover, it is “neutral” in relation to the users. That is, “the model does not need to assume anything about people’s motivations, biases or moods or hashtag formats. The model’s only assumption is that users and hashtags are aligned with a subject of preference, which is why it works regardless of the communication event analyzed, ”the researchers underlined.

A model adaptable to other social media

The researchers indicate that this new ecological approach opens the door to modeling other social media and communication systems, as long as “there is competition for attention, through words or even images, as there would be. the case of Instagram ”. In this sense, the team that participated in the study aims to continue to explore this framework and reflect on future avenues of research, such as the possibility of using these models to intervene in communication events. “In the same way that ecologists use their models to try to intervene in ecosystems to, for example, prevent the extinction of a certain species, our idea is to carry out theoretical research on the conditions under which these events of communication strengthens or fades with a view to possible future intervention. For example, to make certain conversations or pernicious hashtags disappear, such as those produced in fake news bubbles, ”the authors conclude.

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The recent scientific publication on this research corresponds to the last chapter of the thesis written by María José Palazzi as part of the UOC doctoral program in Network and Information Technologies. This article is the result of a triple collaboration between its UOC research group, CoSIN3, the University of Padua and the Spanish Institute for Interdisciplinary Physics and Complex Systems (IFISC), which is attached to the Spanish National Council of research (CSIC) and the University of the Balearic Islands, within the framework of the project Towards an ecological approach to information systems (TEAMS), funded by the Fondazione Cassa di Risparmio di Padova e Rovigo.

This UOC research supports Sustainable Development Goals (SDGs) 9, industry, innovation and infrastructure, and 16, peace, justice and strong institutions.

UOC R&I

UOC’s research and innovation (R&I) helps overcome the pressing challenges facing global societies in the 21st century, by studying the interactions between technology and the humanities and social sciences with a particular focus on the emerging society. network, e-learning and e-health. More than 500 researchers and 51 research groups work in the seven faculties and two research centers of the University: the Interdisciplinary Institute of the Internet (IN3) and the Online Health Center (eHC).

The United Nations 2030 Agenda for Sustainable Development and open knowledge serve as strategic pillars for UOC’s education, research and innovation. More information: research.uoc.edu. # UOC25years



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