Mathematics and physics applications in sociodynamics simulation: the case of opinion formation and diffusion

Tolerance-based interaction: A new model targeting opinion formation and diffusion in social networks

10.7287/peerj.preprints.1322v1 ◽

2015 ◽

Author(s):

Alexandru Topirceanu ◽

Mihai Udrescu ◽

Mircea Vladutiu ◽

Radu Marculescu

Keyword(s):

Phase Transition ◽

Social Network ◽

Discrete Event ◽

Interaction Model ◽

Population Level ◽

Network Size ◽

Opinion Formation ◽

Social Agent ◽

Real World Datasets ◽

And Diffusion

One of the main motivations behind social network analysis is the quest for understanding opinion formation and diffusion. Previous models have limitations, as they typically assume opinion interaction mechanisms based on thresholds which are either fixed or evolve according to a random process that is external to the social agent. Indeed, our empirical analysis on large real-world datasets such as Twitter, Meme Tracker, and Yelp, uncovers previously unaccounted for dynamic phenomena at population-level, namely the existence of distinct opinion formation phases and social balancing. We also reveal that a phase transition from an erratic behavior to social balancing can be triggered by network topology and by the ratio of opinion sources. Consequently, in order to build a model that properly accounts for these phenomena, we propose a new (individual-level) opinion interaction model based on tolerance. As opposed to the existing opinion interaction models, the new tolerance model assumes that individual's inner willingness to accept new opinions evolves over time according to basic human traits. Finally, by employing discrete event simulation on diverse social network topologies, we validate our opinion interaction model and show that, although the network size and opinion source ratio are important, the phase transition to social balancing is mainly fostered by the democratic structure of the small-world topology.

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Tolerance-based interaction: a new model targeting opinion formation and diffusion in social networks

PeerJ Computer Science ◽

10.7717/peerj-cs.42 ◽

2016 ◽

Vol 2 ◽

pp. e42 ◽

Cited By ~ 10

Author(s):

Alexandru Topirceanu ◽

Mihai Udrescu ◽

Mircea Vladutiu ◽

Radu Marculescu

Keyword(s):

Phase Transition ◽

Social Network ◽

Discrete Event ◽

Interaction Model ◽

Population Level ◽

Network Size ◽

Opinion Formation ◽

Social Agent ◽

Real World Datasets ◽

And Diffusion

One of the main motivations behind social network analysis is the quest for understanding opinion formation and diffusion. Previous models have limitations, as they typically assume opinion interaction mechanisms based on thresholds which are either fixed or evolve according to a random process that is external to the social agent. Indeed, our empirical analysis on large real-world datasets such as Twitter, Meme Tracker, and Yelp, uncovers previously unaccounted for dynamic phenomena at population-level, namely the existence of distinctopinion formation phasesandsocial balancing. We also reveal that a phase transition from an erratic behavior to social balancing can be triggered by network topology and by the ratio of opinion sources. Consequently, in order to build a model that properly accounts for these phenomena, we propose a new (individual-level) opinion interaction model based on tolerance. As opposed to the existing opinion interaction models, the new tolerance model assumes that individual’s inner willingness to accept new opinions evolves over time according to basic human traits. Finally, by employing discrete event simulation on diverse social network topologies, we validate our opinion interaction model and show that, although the network size and opinion source ratio are important, the phase transition to social balancing is mainly fostered by the democratic structure of the small-world topology.

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Tolerance-based interaction: A new model targeting opinion formation and diffusion in social networks

10.7287/peerj.preprints.1322v2 ◽

2015 ◽

Author(s):

Alexandru Topirceanu ◽

Mihai Udrescu ◽

Mircea Vladutiu ◽

Radu Marculescu

Keyword(s):

Phase Transition ◽

Social Network ◽

Discrete Event ◽

Interaction Model ◽

Population Level ◽

Network Size ◽

Opinion Formation ◽

Social Agent ◽

Real World Datasets ◽

And Diffusion

One of the main motivations behind social network analysis is the quest for understanding opinion formation and diffusion. Previous models have limitations, as they typically assume opinion interaction mechanisms based on thresholds which are either fixed or evolve according to a random process that is external to the social agent. Indeed, our empirical analysis on large real-world datasets such as Twitter, Meme Tracker, and Yelp, uncovers previously unaccounted for dynamic phenomena at population-level, namely the existence of distinct opinion formation phases and social balancing. We also reveal that a phase transition from an erratic behavior to social balancing can be triggered by network topology and by the ratio of opinion sources. Consequently, in order to build a model that properly accounts for these phenomena, we propose a new (individual-level) opinion interaction model based on tolerance. As opposed to the existing opinion interaction models, the new tolerance model assumes that individual's inner willingness to accept new opinions evolves over time according to basic human traits. Finally, by employing discrete event simulation on diverse social network topologies, we validate our opinion interaction model and show that, although the network size and opinion source ratio are important, the phase transition to social balancing is mainly fostered by the democratic structure of the small-world topology.

Download Full-text

Tolerance-based interaction: A new model targeting opinion formation and diffusion in social networks

10.7287/peerj.preprints.1322 ◽

2015 ◽

Author(s):

Alexandru Topirceanu ◽

Mihai Udrescu ◽

Mircea Vladutiu ◽

Radu Marculescu

Keyword(s):

Phase Transition ◽

Social Network ◽

Discrete Event ◽

Interaction Model ◽

Population Level ◽

Network Size ◽

Opinion Formation ◽

Social Agent ◽

Real World Datasets ◽

And Diffusion

One of the main motivations behind social network analysis is the quest for understanding opinion formation and diffusion. Previous models have limitations, as they typically assume opinion interaction mechanisms based on thresholds which are either fixed or evolve according to a random process that is external to the social agent. Indeed, our empirical analysis on large real-world datasets such as Twitter, Meme Tracker, and Yelp, uncovers previously unaccounted for dynamic phenomena at population-level, namely the existence of distinct opinion formation phases and social balancing. We also reveal that a phase transition from an erratic behavior to social balancing can be triggered by network topology and by the ratio of opinion sources. Consequently, in order to build a model that properly accounts for these phenomena, we propose a new (individual-level) opinion interaction model based on tolerance. As opposed to the existing opinion interaction models, the new tolerance model assumes that individual's inner willingness to accept new opinions evolves over time according to basic human traits. Finally, by employing discrete event simulation on diverse social network topologies, we validate our opinion interaction model and show that, although the network size and opinion source ratio are important, the phase transition to social balancing is mainly fostered by the democratic structure of the small-world topology.

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Meridional Circulation, Turbulence, and Diffusion Processes in Stars

International Astronomical Union Colloquium ◽

10.1017/s0252921100080477 ◽

1976 ◽

Vol 32 ◽

pp. 109-116 ◽

Cited By ~ 1

Author(s):

S. Vauclair

Keyword(s):

Convection Zone ◽

Diffusion Processes ◽

Meridional Circulation ◽

Diffusion Time ◽

Work In Progress ◽

First Results ◽

Stellar Envelopes ◽

And Diffusion ◽

Turbulence And Diffusion ◽

Hr Diagram

This paper gives the first results of a work in progress, in collaboration with G. Michaud and G. Vauclair. It is a first attempt to compute the effects of meridional circulation and turbulence on diffusion processes in stellar envelopes. Computations have been made for a 2 Mʘstar, which lies in the Am - δ Scuti region of the HR diagram.Let us recall that in Am stars diffusion cannot occur between the two outer convection zones, contrary to what was assumed by Watson (1970, 1971) and Smith (1971), since they are linked by overshooting (Latour, 1972; Toomre et al., 1975). But diffusion may occur at the bottom of the second convection zone. According to Vauclair et al. (1974), the second convection zone, due to He II ionization, disappears after a time equal to the helium diffusion time, and then diffusion may happen at the bottom of the first convection zone, so that the arguments by Watson and Smith are preserved.

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