Modelling behavioural contagion

House, Thomas

doi:10.1098/rsif.2011.0014

Cited by 29 publications

(23 citation statements)

References 15 publications

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“…Therefore, the information spreading in online social networks should be much different from the epidemic spreading or the off-line information spreading at the microscopic or macroscopic level. To date, some models that describe information spreading process have been presented [18,19], but there is still not a clear picture on this issue. In 2010, Centola conducted an experiment of behavior spreading in online social networks; although behavior and information are different disseminules, we believe they share some similar properties and the behavior spreading process will make use of the information spreading.…”

Section: Introductionmentioning

confidence: 99%

Spreading in online social networks: The role of social reinforcement

2013

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Some epidemic spreading models are usually applied to analyze the propagation of opinions or news. However, the dynamics of epidemic spreading and information or behavior spreading are essentially different in many aspects. Centola's experiments [Science 329, 1194[Science 329, (2010] on behavior spreading in online social networks showed that the spreading is faster and broader in regular networks than in random networks. This result contradicts with the former understanding that random networks are preferable for spreading than regular networks. To describe the spreading in online social networks, a unknown-known-approved-exhausted four-status model was proposed, which emphasizes the effect of social reinforcement and assumes that the redundant signals can improve the probability of approval (i.e., the spreading rate). Performing the model on regular and random networks, it is found that our model can well explain the results of Centola's experiments on behavior spreading and some former studies on information spreading in different parameter space. The effects of average degree and network size on behavior spreading process are further analyzed. The results again show the importance of social reinforcement and are accordant with Centola's anticipation that increasing the network size or decreasing the average degree will enlarge the difference of the density of final approved nodes between regular and random networks. Our work complements the former studies on spreading dynamics, especially the spreading in online social networks where the information usually requires individuals' confirmations before being transmitted to others.

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Section: Introductionmentioning

confidence: 99%

Spreading in online social networks: The role of social reinforcement

2013

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“…Many social networks exhibit clustering, and so the status of one neighbour v of u is likely to be correlated to the status of another neighbour w. This assumptions of independence of neighbours breaks down. For this case, ad hoc approaches are able to make some progress [35]. However, in particular limits analytic results are possible.…”

Section: Triangle-based Networkmentioning

confidence: 99%

Complex contagions and hybrid phase transitions

Miller

2015

jcomplexnetw

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A complex contagion is an infectious process in which individuals may require multiple transmissions before changing state. These are used to model behaviours if an individual only adopts a particular behaviour after perceiving a consensus among others. We may think of individuals as beginning inactive and becoming active once they are contacted by a sufficient number of active partners. These have been studied in a number of cases, but analytic models for the dynamic spread of complex contagions are typically complex. Here we study the dynamics of the Watts threshold model (WTM) assuming that transmission occurs in continuous time as a Poisson process, or in discrete time where individuals transmit to all partners in the time step following their infection. We adapt techniques developed for infectious disease modelling to develop an analyse analytic models for the dynamics of the WTM in configuration model networks and a class of random clustered (triangle-based) networks. The resulting model is relatively simple and compact. We use it to gain insights into the dynamics of the contagion. Taking the infinite population limit, we derive conditions under which cascades happen with an arbitrarily small initial proportion active, confirming a hypothesis of Watts for this case. We also observe hybrid phase transitions when cascades are not possible for small initial conditions, but occur for large enough initial conditions. We derive sufficient conditions for this hybrid phase transition to occur. We show that in many cases, if the hybrid phase transition occurs, then all individuals eventually become active. Finally, we discuss the role clustering plays in facilitating or impeding the spread and find that the hypothesis of Watts that was confirmed in configuration model networks does not hold in general. This approach allows us to unify many existing disparate observations and derive new results.

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“…This is rarely the case because it is natural for people to protect themselves when realizing disease risks (Eames, Tilston, Brooks-Pollock, & Edmunds, 2012;Ferguson, 2007). To improve, there has been much recent interest in modeling two diffusion processes in an epidemic, either a behavior-disease diffusion (House, 2011;Mao & Bian, 2011;Vardavas, Breban, & Blower, 2007), or an information (awareness)-disease diffusion (Funk, Gilad, Watkins, & Jansen, 2009;Kiss, Cassell, Recker, & Simon, 2010). These 'dual-diffusion' models have made a remarkable progress toward the reality, but none of them consider all the three diffusion processes together.…”

Section: Introductionmentioning

confidence: 99%

Modeling triple-diffusions of infectious diseases, information, and preventive behaviors through a metropolitan social network—An agent-based simulation

Mao

2014

Applied Geography

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a b s t r a c tA typical epidemic often involves the transmission of a disease, the flow of information regarding the disease, and the spread of human preventive behaviors against the disease. These three processes diffuse simultaneously through human social networks, and interact with one another, forming negative and positive feedback loops in the complex human-disease systems. Few studies, however, have been devoted to coupling all the three diffusions together and representing their interactions. To fill the knowledge gap, this article proposes a spatially explicit agent-based model to simulate a triple-diffusion process in a metropolitan area of 1 million people. The individual-based approach, network model, behavioral theories, and stochastic processes are used to formulate the three diffusions and integrate them together. Compared to the observed facts, the model results reasonably replicate the trends of influenza spread and information propagation. The model thus could be a valid and effective tool to evaluate information/behavior-based intervention strategies. Besides its implications to the public health, the research findings also contribute to network modeling, systems science, and medical geography.

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Modelling behavioural contagion

Cited by 29 publications

References 15 publications

Spreading in online social networks: The role of social reinforcement

Spreading in online social networks: The role of social reinforcement

Complex contagions and hybrid phase transitions

Modeling triple-diffusions of infectious diseases, information, and preventive behaviors through a metropolitan social network—An agent-based simulation

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