  We introduce the problem of proficiency modeling: Given a user's posts on a
social media platform, the task is to identify the subset of posts or topics
for which the user has some level of proficiency. This enables the filtering
and ranking of social media posts on a given topic as per user proficiency.
Unlike experts on a given topic, proficient users may not have received formal
training and possess years of practical experience, but may be autodidacts,
hobbyists, and people with sustained interest, enabling them to make genuine
and original contributions to discourse. While predicting whether a user is an
expert on a given topic imposes strong constraints on who is a true positive,
proficiency modeling implies a graded scoring, relaxing these constraints. Put
another way, many active social media users can be assumed to possess, or
eventually acquire, some level of proficiency on topics relevant to their
community. We tackle proficiency modeling in an unsupervised manner by
utilizing user embeddings to model engagement with a given topic, as indicated
by a user's preference for authoring related content. We investigate five
alternative approaches to model proficiency, ranging from basic ones to an
advanced, tailored user modeling approach, applied within two real-world
benchmarks for evaluation.
