Abstract
Information spreading processes are a key phenomenon observed within real and digital social networks. Network members are often under pressure from incoming information with different sources, such as informative campaigns for increasing awareness, viral marketing, rumours, fake news, or the results of other activities. Messages are often repeated, and such repetition can improve performance in the form of cumulative influence. Repeated messages may also be ignored due to a limited ability to process information. Learning processes are leading to the repeated messages being ignored, as their content has already been absorbed. In such cases, responsiveness decreases with repetition, and the habituation effect can be observed. Here, we analyse spreading processes while considering the habituation effect and performance drop along with an increased number of contacts. The ability to recover when reducing the number of messages is also considered. The results show that even low habituation and a decrease in propagation probability may substantially impact network coverage. This can lead to a significant reduction in the potential for a seed set selected with an influence maximisation method. Apart from the impact of the habituation effect on spreading processes, we show how it can be reduced with the use of the sequential seeding approach. This shows that sequential seeding is less sensitive to the habituation effect than single-stage seeding, and that it can be used to limit the negative impact on users overloaded with incoming messages.
Highlights
Information spreading processes are a key phenomenon observed within real and digital social networks
The main goal of the study was to analyse the potential impact of the habituation effect on spreading processes, and to compare the performance with and without incorporating the habituation effect within a spreading model based on the independent cascade model[5]; and, second, to verify the ability to reduce the habituation effect by limiting the intensity of the process through spreading seeds over time using the sequential seeding approach[47]
We investigated how the habituation effect can influence the dynamics of spreading processes
Summary
Information spreading processes are a key phenomenon observed within real and digital social networks. Marketing campaigns benefit from customers recommending products to friends, and diffusion mechanisms can be used to improve their performance They are beneficial from the customer’s point of view, as better targeted content is delivered and unwanted messages are r educed[1]. Several attempts have been made to include repeated contacts within information-spreading models, for the more realistic modelling of real processes. One such approach has assumed cumulative influence from repeated activations, for example, repeated purchases[16]. The probability of purchases generally decreased with the number of received recommendations, with the dynamics being dependent on the product category
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