Evolution of opinions on social networks in the presence of competing committed groups

Public opinion is often affected by the presence of committed groups of individuals dedicated to competing points of view. Using a model of pairwise social influence, we study how the presence of such groups within social networks affects the outcome and the speed of evolution of the overall opinion on the network. Earlier work indicated that a single committed group within a dense social network can cause the entire network to quickly adopt the group's opinion (in times scaling logarithmically with the network size), so long as the committed group constitutes more than about 10% of the population (with the findings being qualitatively similar for sparse networks as well). Here we study the more general case of opinion evolution when two groups committed to distinct, competing opinions $A$ and $B$, and constituting fractions $p_A$ and $p_B$ of the total population respectively, are present in the network. We show for stylized social networks (including Erd\H{o}s-R\'enyi random graphs and Barab\'asi-Albert scale-free networks) that the phase diagram of this system in parameter space $(p_A,p_B)$ consists of two regions, one where two stable steady-states coexist, and the remaining where only a single stable steady-state exists. These two regions are separated by two fold-bifurcation (spinodal) lines which meet tangentially and terminate at a cusp (critical point). We provide further insights to the phase diagram and to the nature of the underlying phase transitions by investigating the model on infinite (mean-field limit), finite complete graphs and finite sparse networks. For the latter case, we also derive the scaling exponent associated with the exponential growth of switching times as a function of the distance from the critical point.Comment: 23 pages: 15 pages + 7 figures (main text), 8 pages + 1 figure + 1 table (supplementary info

New activity pattern in human interactive dynamics

We investigate the response function of human agents as demonstrated by written correspondence, uncovering a new universal pattern for how the reactive dynamics of individuals is distributed across the set of each agent's contacts. In long-term empirical data on email, we find that the set of response times considered separately for the messages to each different correspondent of a given writer, generate a family of heavy-tailed distributions, which have largely the same features for all agents, and whose characteristic times grow exponentially with the rank of each correspondent. We furthermore show that this universal behavioral pattern emerges robustly by considering weighted moving averages of the priority-conditioned response-time probabilities generated by a basic prioritization model. Our findings clarify how the range of priorities in the inputs from one's environment underpin and shape the dynamics of agents embedded in a net of reactive relations. These newly revealed activity patterns might be present in other general interactive environments, and constrain future models of communication and interaction networks, affecting their architecture and evolution.Comment: 15 pages, 7 figure

Threshold-limited spreading in social networks with multiple initiators

A classical model for social-influence-driven opinion change is the threshold model. Here we study cascades of opinion change driven by threshold model dynamics in the case where multiple {\it initiators} trigger the cascade, and where all nodes possess the same adoption threshold $\phi$. Specifically, using empirical and stylized models of social networks, we study cascade size as a function of the initiator fraction $p$. We find that even for arbitrarily high value of $\phi$, there exists a critical initiator fraction $p_c(\phi)$ beyond which the cascade becomes global. Network structure, in particular clustering, plays a significant role in this scenario. Similarly to the case of single-node or single-clique initiators studied previously, we observe that community structure within the network facilitates opinion spread to a larger extent than a homogeneous random network. Finally, we study the efficacy of different initiator selection strategies on the size of the cascade and the cascade window

Voting contagion: modeling and analysis of a century of U.S. presidential elections

Sem informaçãoSocial influence plays an important role in human behavior and decisions. Sources of influence can be divided as external, which are independent of social context, or as originating from peers, such as family and friends. An important question is how to disentangle the social contagion by peers from external influences. While a variety of experimental and observational studies provided insight into this problem, identifying the extent of contagion based on large-scale observational data with an unknown network structure remains largely unexplored. By bridging the gap between the large-scale complex systems perspective of collective human dynamics and the detailed approach of social sciences, we present a parsimonious model of social influence, and apply it to a central topic in political science-elections and voting behavior. We provide an analytical expression of the county vote-share distribution, which is in excellent agreement with almost a century of observed U.S. presidential election data. Analyzing the social influence topography over this period reveals an abrupt phase transition from low to high levels of social contagion, and robust differences among regions. These results suggest that social contagion effects are becoming more instrumental in shaping large-scale collective political behavior, with implications on democratic electoral processes and policies.125130Sem informaçãoSem informaçãoSem informaçã

Analysis and Control of Socio-Cultural Opinion Evolution in Complex Social Systems

The overarching goal of this thesis is to further our understanding about opinion evolution in networked societies. Such insights can be used in a variety of fields such as economy, marketing, transportation, egress, etc. Three main subjects build up this interdisciplinary research: Sociology, Statistical Mechanics, and Network Sciences. In this thesis, for macrolevel (or society-level) analyses, techniques from statistical mechanics have been borrowed to mathematically model the opinion dynamic on different network topologies based on different interaction models. Also, for micro-level (individual-level) analyses, Individual Decision Making Algorithms (IDMA) have been designed. To account for both macro-level and micro-level dynamics, these two regimes are combined resulting in a more accurate model for opinion propagation. Assessing the controllability of such dynamics through experiments in presence of actual humans is the part of this thesis

Network resilience

Many systems on our planet are known to shift abruptly and irreversibly from one state to another when they are forced across a "tipping point," such as mass extinctions in ecological networks, cascading failures in infrastructure systems, and social convention changes in human and animal networks. Such a regime shift demonstrates a system's resilience that characterizes the ability of a system to adjust its activity to retain its basic functionality in the face of internal disturbances or external environmental changes. In the past 50 years, attention was almost exclusively given to low dimensional systems and calibration of their resilience functions and indicators of early warning signals without considerations for the interactions between the components. Only in recent years, taking advantages of the network theory and lavish real data sets, network scientists have directed their interest to the real-world complex networked multidimensional systems and their resilience function and early warning indicators. This report is devoted to a comprehensive review of resilience function and regime shift of complex systems in different domains, such as ecology, biology, social systems and infrastructure. We cover the related research about empirical observations, experimental studies, mathematical modeling, and theoretical analysis. We also discuss some ambiguous definitions, such as robustness, resilience, and stability.Comment: Review chapter

Viewing Trends in Graph Connectivity as Early Warnings of Epidemics and Vaccine Crises

When measles was rampant, suffering apparent, and relief desired, the prospect of vaccination was received with open arms by a grateful public. But it worked \emph{too} well, and opinions slowly diverged; scientists saw aggregate health as proof of the efficacy of intervention, while some of the lay public wondered "But do we really need this vaccine, though? I don't see sick people..." Spurious 1998 research linking the MMR vaccine to autism was published and our dreams of eradication evaporated; the diseases were back to stay. The spread of vaccine disinformation through social networks is immediately apparent and easily exploited, even more so due to the strong assortativity of social networks (both online and face-to-face). Therein lies the focus of this thesis; we investigate different measures of spatial grouping as early warnings signals (EWS) of epidemics through the simulation of social and contact networks and the use of various statistical and graph theoretical tools. Using an agent-based model coupling a binary voting dynamic with an SIRVp model of infection, we simulate a vaccine preventable disease. Each week, agents are given the opportunity to change opinion to that of a friend, while having potentially disease-spreading interactions with many people. The first study confirms that changes in trend of the Moran's I, Geary's C and mutual information statistics give early warnings of the critical transitions representing both vaccine crises and epidemics. This is independent of the strength of an injunctive social norm, though through change point testing we confirm that these warnings come closer to vaccine crises as the norm becomes stronger. We find also that the observable distance between vaccine crisis and epidemic spread decreases as the norm strengthens. Confirmation of these results for other different models boosts our confidence in our results. Our second study shows that graph theoretical changes in incidences of opinion-based communities and echo chambers coincide reliably with outbreaks. Clustering, network modularity and the rate of opinion change also provide EWS of both vaccine crises and epidemics in the population. Due to the immense size and traffic of current social networks, only portions of interactions can be observed at any one time, and therefore our third study tests previously effective signals against an incorporation of vaccine hesitance and network sampling. We find that these identified tools remain good EWS, though experiencing penalties on effectiveness dependent on the sampling rate of the population. In sum, our work provides easily employable tools to predict important negative epidemiological events using readily available data, the best-performing of which is the entropy-based mutual information statistic. Given current and expected events, we believe that this thesis makes a solid contribution to the sparse EWS literature for coupled disease-behaviour systems, as well as providing tools that can be used to inform policy decisions surrounding the mitigation of human folly and critical infection events