The popularity of online social networks and the social interactions they allow has brought great benefits in terms of ease of communication, allowing them to hold a major role in the dissemination and consumption of information. Users of media can be exposed to a wide range of opinions, either actively or passively. Recommendation systems have been developed to steer users towards like-minded content, to the detriment of new, niche, or diverse content. This can lead to fake news, filter bubbles, and opinion polarization. In this paper, we introduce a framework to achieve better diversity in social networks, by formulating information exposure diversity as an optimization problem in which local modifications on the graph, via edge additions, have the objective of maximizing a target diversity metric from the point of view of an user in the network. We formalize the notion of information exposure linking it to well-studied models in the literature, and provide several algorithms for solving this problem, by leveraging gradient descent-based approaches and greedy algorithms. We show experimentally that our algorithms achieve better diversity measures than state-of-the-art algorithms, on a varied set of realworld graphs.