Towards the Identification of Experts in Informal Learning Portals at Scale

Abstract

During the past decade, there has been growing interest among researchers in informal learning at scale, particularly in the area of expert finding. These platforms have played a fundamental role in facilitating informal learning at scale, by providing access to diverse expertise and knowledge resources that might not otherwise be available to learners. Based on the encountered gaps in expert identification in Question & Answer (Q&A) portals, we inspect the feasibility of identifying data science experts in Reddit using the activity behaviour of every user, including Natural Language Processing (NLP), crowdsourced and user features sets. We also examine the impact of using only expert and non-expert classes versus three classes additionally including the out-of-scope class. Our findings can be used for distinguishing different types of users in Reddit, creating a recommendation system, identifying unreliable users or social bots in the early stage and reducing their influence.