Digital advertising plays a growing role in our society and tends to substitute more and more to traditional advertising (like newspapers, TV, billboards). Indeed, companies can minimize their ad costs by targeting directly users/individuals that are potentially interested by their products. We develop several models of targeted advertising with auctions. Each model focuses on a different type of advertising, namely, commercial advertising for triggering purchases or subscriptions, and social marketing for alerting people about unhealthy behaviors (anti-drug, road-safety campaigns). All our models are based on a common framework encoding people's online behaviours and the targeted advertising auction mechanism widely used on Internet. Our main results are to provide semi-explicit formulas for the optimal value and bidding policy for each of these problems. By means of these formulas, we are able to analyze and interpret how phenomenons like people's online behaviors and social interactions affect the optimal bid for targeted advertising auctions. We also study how to efficiently combine targeted advertising and non-targeted advertising mechanism. We conclude by providing some classes of examples with fully explicit formulas. Based on joint work with Médéric Motte (Université de Paris).