The far-right’s influence on Twitter during the 2018 Andalusian elections: an approach through political leaders

New technologies allow politicians to spread their messages omitting the role of mediators. In this context, the Internet has also promoted the emergence of a new actor, digital opinion leaders, who go beyond traditional politics and seek to set the public agenda. One of the main questions nowadays is whether social media, and in particular Twitter as a consolidated tool for political communication, is only used as a sounding board for their political statements, spurring the messages of populist forces. With this in mind, the main objective of this research is to explore the influence of the far-right in the public debate of political leaders on Twitter, analyzing the specific case of the Andalusian regional elections held in December 2018. These elections can be considered a political turning point, with an extreme right party winning seats in a Spanish regional election for the first time in 35 years. In this paper we analyze if Vox used a differentiated strategy via this social network compared to the candidates of the traditional parties: PSOE, PP, Ciudadanos, and Adelante Andalucía. Using content analysis on Twitter as a method, this research determines how Vox candidates worked as influencers of the digital political debate, despite being extra-parliamentary. Vox marked the agenda for the rest of the leaders, while generating great expectation among the audience

Dissemination of Health Information within Social Networks

In this paper, we investigate, how information about a common food born health hazard, known as Campylobacter, spreads once it was delivered to a random sample of individuals in France. The central question addressed here is how individual characteristics and the various aspects of social network influence the spread of information. A key claim of our paper is that information diffusion processes occur in a patterned network of social ties of heterogeneous actors. Our percolation models show that the characteristics of the recipients of the information matter as much if not more than the characteristics of the sender of the information in deciding whether the information will be transmitted through a particular tie. We also found that at least for this particular advisory, it is not the perceived need of the recipients for the information that matters but their general interest in the topic

Identifying communities by influence dynamics in social networks

Communities are not static; they evolve, split and merge, appear and disappear, i.e. they are product of dynamical processes that govern the evolution of the network. A good algorithm for community detection should not only quantify the topology of the network, but incorporate the dynamical processes that take place on the network. We present a novel algorithm for community detection that combines network structure with processes that support creation and/or evolution of communities. The algorithm does not embrace the universal approach but instead tries to focus on social networks and model dynamic social interactions that occur on those networks. It identifies leaders, and communities that form around those leaders. It naturally supports overlapping communities by associating each node with a membership vector that describes node's involvement in each community. This way, in addition to overlapping communities, we can identify nodes that are good followers to their leader, and also nodes with no clear community involvement that serve as a proxy between several communities and are equally as important. We run the algorithm for several real social networks which we believe represent a good fraction of the wide body of social networks and discuss the results including other possible applications.Comment: 10 pages, 6 figure

E-learning adoption in universities: the ‘gazebo’ effect of the social system on diffusion.

The implementation of e-learning in universities is often explored through the conceptual framework of the innovation diffusion model (Rogers 2003). Analysis using the five adopter categories or the characteristics of the innovation is common, but a less frequently explored element is the influence on diffusion of the social system within which the individual adopters are situated. The paper considers the potential of this element of Rogers’ model to explain the diffusion of e-learning within the social system of a university and demonstrates that the nature of universities, traditionally considered to be highly decentralized organizations composed of many ‘ivory gazebos’ rather than a single ‘ivory tower’, may expose some challenges to the usefulness of the model. Factors considered include the ambiguity of management positions and the nature of communication in devolved departments

Partial containment control over signed graphs

In this paper, we deal with the containment control problem in presence of antagonistic interactions. In particular, we focus on the cases in which it is not possible to contain the entire network due to a constrained number of control signals. In this scenario, we study the problem of selecting the nodes where control signals have to be injected to maximize the number of contained nodes. Leveraging graph condensations, we find a suboptimal and computationally efficient solution to this problem, which can be implemented by solving an integer linear problem. The effectiveness of the selection strategy is illustrated through representative simulations.Comment: 6 pages, 3 figures, accepted for presentation at the 2019 European Control Conference (ECC19), Naples, Ital

Effects of Network Communities and Topology Changes in Message-Passing Computation of Harmonic Influence in Social Networks

The harmonic influence is a measure of the importance of nodes in social networks, which can be approximately computed by a distributed message-passing algorithm. In this extended abstract we look at two open questions about this algorithm. How does it perform on real social networks, which have complex topologies structured in communities? How does it perform when the network topology changes while the algorithm is running? We answer these two questions by numerical experiments on a Facebook ego network and on synthetic networks, respectively. We find out that communities can introduce artefacts in the final approximation and cause the algorithm to overestimate the importance of "local leaders" within communities. We also observe that the algorithm is able to adapt smoothly to changes in the topology.Comment: 4 pages, 7 figures, submitted as conference extended abstrac