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.

信息传播过程是在真实和数字社交网络中观察到的一个关键现象。网络成员经常面临来自不同来源的传入信息的压力，例如旨在提高认识的信息活动、病毒式营销、谣言、虚假新闻或其他活动的结果。消息经常被重复，这种重复可以以累积影响的形式提高绩效。由于处理信息的能力有限，重复的消息也可能被忽略。学习过程导致重复的信息被忽略，因为它们的内容已经被吸收了。在这种情况下，反应能力会随着重复而降低，并且可以观察到习惯化效应。在这里，我们分析传播过程，同时考虑随着接触数量的增加而产生的习惯效应和性能下降。还考虑了减少消息数量时的恢复能力。结果表明，即使习惯程度较低和传播概率降低也可能会严重影响网络覆盖范围。这可能会导致使用影响力最大化方法选择种子集的潜力显着降低。除了习惯效应对传播过程的影响之外，我们还展示了如何通过使用顺序播种方法来减少习惯效应。这表明顺序播种对习惯效应的敏感度低于单阶段播种，并且它可以用来限制对传入消息过载的用户的负面影响。