  The irreplaceable key to the triumph of Question & Answer (Q&A) platforms is
their users providing high-quality answers to the challenging questions posted
across various topics of interest. From more than a decade, the expert finding
problem attracted much attention in information retrieval research. Based on
the encountered gaps in the expert identification across several Q&A portals,
we inspect the feasibility of identifying data science experts in Reddit. Our
method is based on the manual coding results where two data science experts
labelled not only expert and non-expert comments, but also out-of-scope
comments, which is a novel contribution to the literature, enabling the
identification of more groups of comments across web portals. We present a
semi-supervised approach which combines 1,113 labelled comments with 100,226
unlabelled comments during training. The proposed model uses the activity
behaviour of every user, including Natural Language Processing (NLP),
crowdsourced and user feature sets. We conclude that the NLP and user feature
sets contribute the most to the better identification of these three classes.
It means that this method can generalise well within the domain. Finally, we
make a novel contribution by presenting different types of users in Reddit,
which opens many future research directions.
