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 this http URL 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 this http URL 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.
Simulation and probabilistic models as tools for the analysis of online communities has been established in recent years. use simulations to support policy modelling and strategic decisions @cite_2 . The dynamics of popularity among news stories was investigated and described by Hogg and Lerman @cite_12 @cite_1 . Self-enforcing content generation mechanisms and their representation as stochastic processes were investigated in @cite_3 .
@cite_3 @cite_1 @cite_12 @cite_2
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Online peer production systems have enabled people to coactively create, share, classify, and rate content on an unprecedented scale. This paper describes strong macroscopic regularities in how people contribute to peer production systems, and shows how these regularities arise from simple dynamical rules. First, it is demonstrated that the probability a person stops contributing varies inversely with the number of contributions he has made. This rule leads to a power law distribution for the number of contributions per person in which a small number of very active users make most of the contributions. The rule also implies that the power law exponent is proportional to the effort required to contribute, as justified by the data. Second, the level of activity per topic is shown to follow a lognormal distribution generated by a stochastic reinforcement mechanism. A small number of very popular topics thus accumulate the vast majority of contributions. These trends are demonstrated to hold across hundreds of millions of contributions to four disparate peer production systems of differing scope, interface style, and purpose. The popularity of content in social media is unequally distributed, with some items receiving a disproportionate share of attention from users. Predicting which newly-submitted items will become popular is critically important for both the hosts of social media content and its consumers. Accurate and timely prediction would enable hosts to maximize revenue through differential pricing for access to content or ad placement. Prediction would also give consumers an important tool for filtering the content. Predicting the popularity of content in social media is challenging due to the complex interactions between content quality and how the social media site highlights its content. Moreover, most social media sites selectively present content that has been highly rated by similar users, whose similarity is indicated implicitly by their behavior or explicitly by links in a social network. While these factors make it difficult to predict popularity a priori, stochastic models of user behavior on these sites can allow predicting popularity based on early user reactions to new content. By incorporating the various mechanisms through which web sites display content, such models improve on predictions that are based on simply extrapolating from the early votes. Specifically, for one such site, the news aggregator Digg, we show how a stochastic model distinguishes the effect of the increased visibility due to the network from how interested users are in the content. We find a wide range of interest, distinguishing stories primarily of interest to users in the network (“niche interests”) from those of more general interest to the user community. This distinction is useful for predicting a story’s eventual popularity from users’ early reactions to the story. Online social media provide multiple ways to find interesting content. One important method is highlighting content recommended by user’s friends. We examine this process on one such site, the news aggregator Digg. With a stochastic model of user behavior, we distinguish the effects of the content visibility and interestingness to users. We find a wide range of interest and distinguish stories primarily of interest to a users’ friends from those of interest to the entire user community. We show how this model predicts a story’s eventual popularity from users’ early reactions to it, and estimate the prediction reliability. This modeling framework can help evaluate alternative design choices for displaying content on the site. The rapidly increasing popularity of Web 2.0 online communities originates in the ease of collaborative content creation and its sharing. As a result, more community members actively participate in the community and its data growth rates are continuously increasing. This poses the challenge for community platform operators on efficient administration and moderation to ensure the quality of content and to prevent violations of laws (e.g. copyright, privacy, illegal content) and community rules. Involvement of employed administrators who read and approve every piece of user-generated content is clearly the safest way of quality assurance. Since this is a time consuming task it does not scale up with Web 2.0 dimensions. So administrative functions are delegated to members of the community, the moderators. The strategy for choosing trustworthy moderators in big anonymous communities is specified in policies based on user reputation that is measured in bonus points. The proper balancing between community self-management and administration is crucial for the quality, attractiveness, and scalability of the entire community. Therefore understanding the mutual influences between community actors, reputation systems and platform policies that employ user reputation is crucial to ensure the overall quality, user acceptance, and success of the entire online community. Our objective is to predict the behavior in an online community for different policies, which may result in different overall quality of the community content. For this purpose we present our community analysis framework CoSiMo (an acronym for Community Simulation and Modeling), which employs the model-based approach for predicting the impact of policies on community dynamics and health. Through systematic variation of simulated quality assurance mechanisms we show that our model plausibly captures the influence of policies to content quality and can be therefore exploited for optimization of real online communities.