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The term filter bubble has been coined to describe the situation of onlineusers which---due to filtering algorithms---live in a personalised informationuniverse biased towards their own interests.In this paper we use an agent-basedsimulation framework to measure the actual risk and impact of filter bubbleeffects occurring in online communities due to content or author basedpersonalisation algorithms. Observing the strength of filter bubble effectsallows for opposing the benefits to the risks of personalisation.In oursimulation we observed, that filter bubble effects occur as soon as usersindicate preferences towards certain topics.We also saw, that well connectedusers are affected much stronger than average or poorly connected users.Finally, our experimental setting indicated that the employed personalisationalgorithm based on content features seems to bear a lower risk of filter bubbleeffects than one performing personalisation based on authors.

The Impact of the Filter Bubble -- A Simulation Based Framework for Measuring Personalisation Macro Effects in Online Communities