This work considers the flow of information through social networks as consequence of herd behaviour. Herd behaviour is defined as any behaviour in which the probability of acceptance of an idea (or adoption of behaviour) by an individual is a factor of a group adoption. Considering a social network structure, we set a positive probability of a node in the network to accept new ideas. Although Full Herd Behaviour in social decision-making is by no doubt somewhat theoretical, we assume it to exist, to some degree as a factor which affects the acceptance of new ideas in a social network.
For analysis purpose we simulate the change of states over time by synthetically constructed Barabasi-Albert networks. We further assume that the probability of infection follows Herd Behaviour rules. In comparison to the traditional models of infection-spread that have a fix probability of infection in a single encounter, the change of states in our model follow a different infection-spread pattern that better fits an exponential growth phenomena.
A simulated study considered two theoretical models: Group Herd and Global Herd, in which the probability of infection is proportional to the infection rate in the group / full population, respectively. The simulations further show that in global herd model the first 1 % of the spreading is highly stochastic, while later spreading stages can be better predicted. The implications are relevant to viral marketing, spread of rumours, as well as academic publication and awareness.
Although the research is in its preliminary stage and need to be approved against real events data, its initial results seem relevant to the modelling of various social phenomena.
