  Online Social Networks (OSNs) facilitate access to a variety of data allowing
researchers to analyze users' behavior and develop user behavioral analysis
models. These models rely heavily on the observed data which is usually biased
due to the participation inequality. This inequality consists of three groups
of online users: the lurkers - users that solely consume the content, the
engagers - users that contribute little to the content creation, and the
contributors - users that are responsible for creating the majority of the
online content. Failing to consider the contribution of all the groups while
interpreting population-level interests or sentiments may yield biased results.
To reduce the bias induced by the contributors, in this work, we focus on
highlighting the engagers' contributions in the observed data as they are more
likely to contribute when compared to lurkers, and they comprise a bigger
population as compared to the contributors. The first step in behavioral
analysis of these users is to find the topics they are exposed to but did not
engage with. To do so, we propose a novel framework that aids in identifying
these users and estimates their topic exposure. The exposure estimation
mechanism is modeled by incorporating behavioral patterns from similar
contributors as well as users' demographic and profile information.
