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Online social networks (OSN) are prime examples of socio-technical systems inwhich individuals interact via a technical platform. OSN are very volatilebecause users enter and exit and frequently change their interactions. Thismakes the robustness of such systems difficult to measure and to control. Toquantify robustness, we propose a coreness value obtained from the directedinteraction network. We study the emergence of large drop-out cascades of usersleaving the OSN by means of an agent-based model. For agents, we define autility function that depends on their relative reputation and their costs forinteractions. The decision of agents to leave the OSN depends on this utility.Our aim is to prevent drop-out cascades by influencing specific agents with lowutility. We identify strategies to control agents in the core and the peripheryof the OSN such that drop-out cascades are significantly reduced, and therobustness of the OSN is increased.

Improving the robustness of online social networks: A simulation approach of network interventions