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

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