Disincentivizing Polarization in Social Networks
Christian Borgs Jennifer Chayes Christian Ikeokwu Ellen Vitercik
On social networks, algorithmic personalization drives users into filter
bubbles where they rarely see content that deviates from their interests. We
present a model for content curation and personalization that avoids filter
bubbles, along with algorithmic guarantees and nearly matching lower bounds. In
our model, the platform interacts with $n$ users over $T$ timesteps, choosing
content for each user from $k$ categories. The platform receives stochastic
rewards as in a multi-arm bandit. To avoid filter bubbles, we draw on the
intuition that if some users are shown some category of content, then all users
should see at least a small amount of that content. We first analyze a naive
formalization of this intuition and show it has unintended consequences: it
leads to ``tyranny of the majority'' with the burden of diversification borne
disproportionately by those with minority interests. This leads us to our model
which distributes this burden more equitably. We require that the probability
any user is shown a particular type of content is at least $\gamma$ times the
average probability all users are shown that type of content. Full
personalization corresponds to $\gamma = 0$ and complete homogenization
corresponds to $\gamma = 1$; hence, $\gamma$ encodes a hard cap on the level of
personalization. We also analyze additional formulations where the platform can
exceed its cap but pays a penalty proportional to its constraint violation. We
provide algorithmic guarantees for optimizing recommendations subject to these
constraints. These include nearly matching upper and lower bounds for the
entire range of $\gamma \in [0,1]$ showing that the reward of a multi-agent
variant of UCB is nearly optimal. Using real-world preference data, we
empirically verify that under our model, users share the burden of
diversification with only minor utility loss under our constraints.