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We investigate the impact of noise and topology on opinion diversity insocial networks. We do so by extending well-established models of opiniondynamics to a stochastic setting where agents are subject both to assimilativeforces by their local social interactions, as well as to idiosyncratic factorspreventing their population from reaching consensus. We model the latter toaccount for both scenarios where noise is entirely exogenous to peer influenceand cases where it is instead endogenous, arising from the agents' desire tomaintain some uniqueness in their opinions. We derive a general analyticalexpression for opinion diversity, which holds for any network and depends onthe network's topology through its spectral properties alone. Using thisexpression, we find that opinion diversity decreases as communities andclusters are broken down. We test our predictions against data describingempirical influence networks between major news outlets and find thatincorporating our measure in linear models for the sentiment expressed by suchsources on a variety of topics yields a notable improvement in terms ofexplanatory power.

The Impact Of Noise And Topology On Opinion Dynamics In Social Networks