Opinion Dynamics and Communication Networks

This paper examines the interplay of opinion exchange dynamics and communication network formation. An opinion formation procedure is introduced which is based on an abstract representation of opinions as $k$--dimensional bit--strings. Individuals interact if the difference in the opinion strings is below a defined similarity threshold $d_I$. Depending on $d_I$, different behaviour of the population is observed: low values result in a state of highly fragmented opinions and higher values yield consensus. The first contribution of this research is to identify the values of parameters $d_I$ and $k$, such that the transition between fragmented opinions and homogeneity takes place. Then, we look at this transition from two perspectives: first by studying the group size distribution and second by analysing the communication network that is formed by the interactions that take place during the simulation. The emerging networks are classified by statistical means and we find that non--trivial social structures emerge from simple rules for individual communication. Generating networks allows to compare model outcomes with real--world communication patterns.Comment: 14 pages 6 figure

Political opinion dynamics in social networks: The Portuguese 2010-11: Case study

The research on opinion dynamics in social networks and opinion influence models often suffer from a lack of grounding in social theories as well as deficient empirical data validation. The current availability of large datasets, and the ease we can now collect social data from the Internet, makes validation of theoretical social models a less difficult task. Starting by a state-of-the-art of the research and practice concerning political opinion dynamics in social networks, we identify the main strengths and weaknesses of this domain. We then propose a novel method for uncovering political opinion dynamics using on-line data gathering. The method includes three distinct phases: (1) data collection, (2) multi-agent modelling (3) validation. Specifically, we tested the significance of both Social Impact Theory, originally proposed by Latané (1981), and Brownian Agent modelling, proposed by Schweitzer (2002), for characterizing political opinion formation during electoral periods. These two models were tested using more than 100.000 tweets collected during the periods from the 30th of October to the 21st of January 2011 and from the 27th of March to the 6th of June 2011, concerning the Portuguese presidential and legislative elections occurred in 2011. Following the data collection, two distinct on-line communities were inspected: the general Twitter user community, and the traditional news media Twitter feeds. The opinion dynamics was simulated with grid adjustment of model parameters. This operation was performed on separate empirical series, respecting the talk about the six electoral candidates and parties. The complete process allowed concluding about the explanatory power of Social Impact Theory and Brownian Agents, and, on the other side, allowed characterizing opinion dynamics in this specific case study. This article details each phase of the method, illustrated using the dataset available at http://work.theobservatorium.eu/presid2011.info:eu-repo/semantics/acceptedVersio

Measuring agenda-setting effects on Twitter during the 2016 UK EU referendum

This paper investigates first-level agenda-setting effects on Twitter during UK's EU referendum campaign. Using topic-modeling techniques, we investigate the dynamics of the interaction between users and the media content, and how the media outlets relate to each other. Results show that traditional media outlets dominated the debate, but alternative media played an important part. The media outlets that supported “Leave” stood closer to users' opinion who contributed to polarize the media's message, with pro-Leave side successfully framing some media's message in his own terms.info:eu-repo/semantics/acceptedVersio

Community identity in a temporal network: a taxonomy proposal

Communities of nodes are one of the most important meso structures in a network. In static networks they are essentially characterized by the nodes they hold, how modular they are and how they connect to other communities. Once identified, they do not change. In networks that evolve over time, communities can shed and acquire new nodes. This generates new constructs and raises the question of community identity, and of the characterization of the events that define their lifecycle. Although researchers have devoted efforts to address some of these questions, we believe that a formalized classification and a principled method to identify community events is still lacking. In this paper we propose such a classification in the form of a robust taxonomy, supported by a similarity metric based on the Jaccard index but adjusted to chance, and a set of rules that unequivocally can track a community journey from "cradle to grave".info:eu-repo/semantics/acceptedVersio

Caste in the news – a computational analysis of Indian newspapers

Conflicts involving caste issues, mainly concerning the lowest caste rights, pervade modern Indian society. Caste affiliation, being rigorously enforced by the society, is an official contemporary reality. Although caste identity is a major social discrimination, it also serves as a necessary condition for affirmative action like reservation policy. In this article, we perform an original and rigorous analysis of the discourse involving the theme “caste” in India newspapers. To this purpose, we have implemented a computational analysis over a big dataset of the 2016 and 2017 editions of three major Indian newspapers to determine the most salient themes associated with “caste” in the news. We have used an original mix of state-of-the-art algorithms, including those based on statistical distributions and two-layer neural networks, to detect the relevant topics in the news and characterize their linguistic context. We concluded that there is an excessive association between lower castes, victimization, and social unrest in the news that does not adequately cover the reports on other aspects of their life and personal identity, thus reinforcing conflict, while attenuating the vocality and agency of a large section of the population. From our conclusion, we propose a positive discrimination policy in the newsroom.info:eu-repo/semantics/publishedVersio

Second order swarm intelligence

An artificial Ant Colony System (ACS) algorithm to solve general-purpose combinatorial Optimization Problems (COP) that extends previous AC models [21] by the inclusion of a negative pheromone, is here described. Several Travelling Salesman Problem (TSP) were used as benchmark. We show that by using two different sets of pheromones, a second-order co-evolved compromise between positive and negative feedbacks achieves better results than single positive feedback systems. The algorithm was tested against known NP-complete combinatorial Optimization Problems, running on symmetrical TSP's. We show that the new algorithm compares favourably against these benchmarks, accordingly to recent biological findings by Robinson [26,27], and Gruter [28] where "No entry" signals and negative feedback allows a colony to quickly reallocate the majority of its foragers to superior food patches. This is the first time an extended ACS algorithm is implemented with these successful characteristics.info:eu-repo/semantics/acceptedVersio

A taxonomy of community lifecycle events in temporal networks

Communities are one of the most important struc- tural elements of a network. They frequently influence network behavior, which makes their identification especially useful. As a result, community detection has been a popular topic within network science in recent decades. Even more recently, fostered by an increasing availability of time stamped datasets and a pressing realization that most empiric networks are dynamic in nature, temporal networks have attracted increased attention. The time dimension introduces new network constructs and communities are not immune. A community is no longer just a bunch of fixed nodes tightly clustered, but have a life and activity of itself, shedding and gaining nodes, appearing and disappearing on the network. We believe that these dynamic constructs are still lacking a formal, consensual definition. In this article we propose a robust taxonomy of life events for communities and a rules based methodology to clearly parse these events.info:eu-repo/semantics/acceptedVersio

Syntgen: a system to generate temporal networks with user specified topology

In the last few years, the study of temporal networks has progressed markedly. The evolution of clusters of nodes (or communities) is one of the major focus of these studies. However, the time dimension increases complexity, introducing new constructs and requiring novel and enhanced algorithms. In spite of recent improvements, the relative scarcity of timestamped representations of empiric networks, with known ground truth, hinders algorithm validation. A few approaches have been proposed to generate synthetic temporal networks that conform to static topological specifications while in general adopting an ad hoc approach to temporal evolution. We believe there is still a need for a principled synthetic network generator that conforms to problem domain topological specifications from a static as well as temporal perspective. Here, we present such a system. The unique attributes of our system include accepting arbitrary node degree and cluster size distributions and temporal evolution under user control, while supporting tunable joint distribution and temporal correlation of node degrees. Theoretical contributions include the analysis of conditions for graphic sequences of inter- and intracluster node degrees and cluster sizes and the development of a heuristic to search for the cluster membership of nodes that minimizes the shared information distance between clusterings. Our work shows that this system is capable of generating networks under user controlled topology with up to thousands of nodes and hundreds of clusters with strong topology adherence. Much larger networks are possible with relaxed requirements. The generated networks support algorithm validation as well as problem domain analysis.info:eu-repo/semantics/acceptedVersio

Syntgen: A system to generate temporal networks with user specified topology

Network representations can help reveal the behavior of complex systems. Useful information can be derived from the network properties and invariants, such as components, clusters or cliques, as well as from their changes over time. The evolution of clusters of nodes (or communities) is one of the major focus of research. However, the time dimension increases complexity, introducing new constructs and requiring novel and enhanced algorithms. In spite of recent improvements, the relative scarcity of timestamped representations of empiric networks, with known ground truth, hinders algorithm validation. A few approaches have been proposed to generate synthetic temporal networks that conform to static topological specifications while in general adopting an ad-hoc approach to temporal evolution. We believe there is still a need for a principled synthetic network generator that conforms to problem domain topological specifications from a static as well as temporal perspective. Here we present such a system. The unique attributes of our system include accepting arbitrary node degree and cluster size distributions and temporal evolution under user control, while supporting tunable joint distribution and temporal correlation of node degrees. Theoretical contributions include the analysis of conditions for "graphability" of sequences of inter and intra cluster node degrees and cluster sizes and the development of a heuristic to search for the cluster membership of nodes that minimizes the shared information distance between clusterings. Our work shows that this system is capable of generating networks under user controlled topology with up to thousands of nodes and hundreds of clusters with strong topology adherence. Much larger networks are possible with relaxed requirements. The generated networks support algorithm validation as well as problem domain analysis

A model for assessing the quantitative effects of heterogeneous affinity in malaria transmission along with Ivermectin mass administration

Using an agent-based model of malaria, we present numerical evidence that in communities of individuals having an affinity varying within a broad range of values, disease transmission may increase up to 300%. Moreover, our findings provide new insight into how to combine different strategies for the prevention of malaria transmission. In particular, we uncover a relationship between the level of heterogeneity and the level of conventional and unconventional anti-malarial drug administration (ivermectin and gametocidal agents), which, when taken together, will define a control parameter, tuning between disease persistence and elimination. Finally, we also provide evidence that the entomological inoculation rate, as well as the product between parasite and sporozoite rates are both good indicators of malaria incidence in the presence of heterogeneity in disease transmission and may configure a possible improvement in that setting, upon classical standard measures such as the basic reproductive number.info:eu-repo/semantics/publishedVersio