Privacy of Internet users is at stake because they expose personal information in posts created in online communities, in search queries, and other activities. An adversary that monitors a community may identify the users with the most sensitive properties and utilize this knowledge against them (e.g., by adjusting the pricing of goods or targeting ads of sensitive nature). Existing privacy models for structured data are inadequate to capture privacy risks from user posts. This paper presents a ranking-based approach to the as- sessment of privacy risks emerging from textual contents in online communities, focusing on sensitive topics, such as being depressed. We propose ranking as a means of modeling a rational adversary who targets the most aicted users. To capture the adversary's background knowledge regard- ing vocabulary and correlations, we use latent topic mod- els. We cast these considerations into the new model of R- Susceptibility, which can inform and alert users about their potential for being targeted, and devise measures for quantitative risk assessment. Experiments with real-world data show the feasibility of our approach.