Estimating Topic Exposure for Under-Represented Users on Social Media
This work addresses bias in social media research for more accurate population-level interest analysis, though it is incremental as it builds on existing models by focusing on a specific user group.
The paper tackled the problem of biased behavioral analysis in social media due to participation inequality by proposing a framework to estimate topic exposure for under-represented engagers, resulting in a method that identifies these users and models their exposure using behavioral patterns and demographic data.
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.