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Information spreading processes are a key phenomenon observed within real anddigital social networks. Network members are often under pressure from incominginformation with different sources, such as informative campaigns forincreasing awareness, viral marketing, rumours, fake news, or the results ofother activities. Messages are often repeated, and such repetition can improveperformance in the form of cumulative influence. Repeated messages may also beignored due to a limited ability to process information. Learning processes areleading to the repeated messages being ignored, as their content has alreadybeen absorbed. In such cases, responsiveness decreases with repetition, and thehabituation effect can be observed. Here, we analyse spreading processes whileconsidering the habituation effect and performance drop along with an increasednumber of contacts. The the ability to recover when reducing the number ofmessages is also considered. The results show that even low habituation and adecrease in propagation probability may substantially impact network coverage.This can lead to a significant reduction in the potential for a seed setselected with an influence maximisation method. Apart from the impact of thehabituation effect on spreading processes, we show how it can be reduced withthe use of the sequential seeding approach. This shows that sequential seedingis less sensitive to the habituation effect than single-stage seeding, and thatit can be used to limit the negative impact on users overloaded with incomingmessages.

Habituation Effect in Social Networks as a Potential Factor Silently Crushing Influence Maximisation Efforts