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Timeline algorithms are key parts of online social networks, but during recent years they have been blamed for increasing polarization and disagreement in our society. Opinion-dynamics models have been used to study a variety of phenomena in online social networks, but an open question remains on how these models can be augmented to take into account the fine-grained impact of user-level timeline algorithms. We make progress on this question by providing a way to model the impact of timeline algorithms on opinion dynamics. Specifically, we show how the popular Friedkin--Johnsen opinion-formation model can be augmented based on aggregate information, extracted from timeline data. We use our model to study the problem of minimizing the polarization and disagreement; we assume that we are allowed to make small changes to the users' timeline compositions by strengthening some topics of discussion and penalizing some others. We present a gradient descent-based algorithm for this problem, and show that under realistic parameter settings, our algorithm computes a (1+) -approximate solution in time O (mn (1/) ), where m is the number of edges in the graph and n is the number of vertices. We also present an algorithm that provably computes an -approximation of our model in near-linear time. We evaluate our method on real-world data and show that it effectively reduces the polarization and disagreement in the network. Finally, we release an anonymized graph dataset with ground-truth opinions and more than 27\, 000 nodes (the previously largest publicly available dataset contains less than 550 nodes).
Zhou et al. (Thu,) studied this question.