Collaborative, Trusted and Privacy-Aware e/m-Services, Springer, Berlin, Heidelberg, 2013
The understanding and modeling of social dynamics in a complex and unpredictable world, emerges a... more The understanding and modeling of social dynamics in a complex and unpredictable world, emerges as a research target of particular importance. Success in this direction can yield valuable knowledge as to how social phenomena form and evolve in varying socioeconomic contexts comprising economic crises, societal disasters, cultural differences and security threats among others. The study of social dynamics occurring in the aforementioned contexts with the methodological tools originating from the complexity theory, is the research approach we propose in this paper. Furthermore, considering the fact that online social media serve as platforms of individual expression and public dialogue, we anticipate that their study as complex adaptive systems, will significantly contribute to understanding, predicting and monitoring social phenomena taking place on both online and offline social networks.
Bookmarks Related papers MentionsView impact
Uploads
Papers
uence characteristics, time-varying propagation patterns emerge re
ecting the temporal structure, strength, and signal-to-noise ratio characteristics of the stimulation driving the online users' information sharing activity. The proposed model constitutes an overarching, novel, and flexible approach to the modeling of the micro-level mechanisms
whereby information propagates in online social networks. As such, it can be used for a comprehensive understanding of the online transmission of information, a process integral to the sociocultural evolution of modern societies. The proposed model is highly adaptable and suitable for the study of the propagation patterns of behavior, opinions, and innovations among others.
uence characteristics, time-varying propagation patterns emerge re
ecting the temporal structure, strength, and signal-to-noise ratio characteristics of the stimulation driving the online users' information sharing activity. The proposed model constitutes an overarching, novel, and flexible approach to the modeling of the micro-level mechanisms
whereby information propagates in online social networks. As such, it can be used for a comprehensive understanding of the online transmission of information, a process integral to the sociocultural evolution of modern societies. The proposed model is highly adaptable and suitable for the study of the propagation patterns of behavior, opinions, and innovations among others.
In this talk I will present a modeling approach to dynamic patterns of online activity meeting the aforementioned challenges, thereby providing an overarching and generalizable framework for social simulations and human dynamics research. The proposed model lies at the cross-borders of complex systems, neuroscience, artificial intelligence, big data, computer science, mathematics, sociology, and psychology. Individuals are modeled as leaky Integrate-and-Fire neurons driven by endogenous and exogenous stimuli modulating their behavioral state, while firing thresholds and refractoriness regulate their activation pattern. The time-dependent characteristics of the received stimuli render the interconnected individuals a non-autonomous network dynamical system producing time-varying population activity patterns reflecting the temporal structure and strength of the stimuli driving the activation process. By analyzing online activity patterns related to discussions on Twitter, we derive their characteristics and precisely reproduce the temporal and statistical properties of the empirical observations in a qualitative and quantitative way through simulations. The accurate replication of real online activity patterns demonstrates the capacity of the proposed approach to serve as a state-of-the-art platform of social simulations aiming at understanding, explaining, but also predicting the evolution of social dynamical processes.
The proposed method incorporates the dynamics of epidemiological models, of complex contagion, and of open systems. In addition, it features substantial flexibility capable of accommodating heterogeneity in the individuals’ behavioral characteristics, connectivity, and received stimulus. Through various interpretations of the model parameters it is possible to represent the dynamics of diverse socio-economic phenomena, such as opinion formation, emotional contagion, market behavior, adoption of products, viral marketing, and spread of rumors among others. The capacity of the model to accurately fit real activity patterns in conjunction with the availability of social activity data which can be used for the estimation of the model parameters, break new ground in the simulation of social phenomena. As such, the proposed model provides a framework for the study of macro-level activity patterns emerging from the micro-level dynamics of interconnected individuals driven by self-generated, interpersonal and external influences.