Abstract

This paper sets the stage for our primary objective, which is to identify and examine various forms of claimed expertise in anonymous online interactions. By building upon the findings and incorporating the proposed enhancements, we aim to gain a deeper understanding of the nature and implications of different expertise claims within the context of power hierarchies. A combination of various machine learning techniques is employed in this work, including classical methods, deep learning models, and transformer-based approaches to create classification models, while using three datasets collected by specialists and annotated by linguistics experts. The first experiments’ results in binary classification, indicating whether a given post reflects expertise or not, are particularly promising, especially when utilising transformer-based approaches. The second set of experiments, focusing on the classification of different types of expertise, produced a diverse range of results with the less favourable results primarily caused by an imbalance in labelling between different classes.
Original languageEnglish
Title of host publicationAdvances in Computational Intelligence Systems (UKCI 2023)
EditorsNitin Naik, Paul Jenkins, Paul Grace, Longzhi Yang, Shaligram Prajapat
Pages133-139
ISBN (Electronic)9783031475085
DOIs
Publication statusE-pub ahead of print - 1 Feb 2024
EventThe 22nd UK Workshop on Computational Intelligence - Aston University, Birmingham, United Kingdom
Duration: 6 Sept 20238 Sept 2023
https://sites.google.com/view/ukci-2023/home

Publication series

NameAdvances in Intelligent Systems and Computing (AISC)
PublisherSpringer
Volume1453
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

ConferenceThe 22nd UK Workshop on Computational Intelligence
Abbreviated titleUKCI 2023
Country/TerritoryUnited Kingdom
CityBirmingham
Period6/09/238/09/23
Internet address

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