Browsing by Subject "Data driven evaluation"

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    Computational approaches to detect experts in distributed online communities: a case study on Reddit
    (Springer, 2024-04) Strukova, Sofia; Gómez Mármol, Félix; Ruipérez Valiente, José Antonio; Ingeniería de la Información y las Comunicaciones
    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 1113 labelled comments with 100,226 unlabelled comments during training. We proved that it is possible to develop models that can identify expert, non-expert and out-of-scope comments peaking the AUC score at 0.93, accuracy at 0.83, MAE at 0.15 degrees and R2 score at 0.69. 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.