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 language | English |
---|---|
Title of host publication | Advances in Computational Intelligence Systems (UKCI 2023) |
Editors | Nitin Naik, Paul Jenkins, Paul Grace, Longzhi Yang, Shaligram Prajapat |
Pages | 133-139 |
ISBN (Electronic) | 9783031475085 |
DOIs | |
Publication status | E-pub ahead of print - 1 Feb 2024 |
Event | The 22nd UK Workshop on Computational Intelligence - Aston University, Birmingham, United Kingdom Duration: 6 Sept 2023 → 8 Sept 2023 https://sites.google.com/view/ukci-2023/home |
Publication series
Name | Advances in Intelligent Systems and Computing (AISC) |
---|---|
Publisher | Springer |
Volume | 1453 |
ISSN (Print) | 2194-5357 |
ISSN (Electronic) | 2194-5365 |
Conference
Conference | The 22nd UK Workshop on Computational Intelligence |
---|---|
Abbreviated title | UKCI 2023 |
Country/Territory | United Kingdom |
City | Birmingham |
Period | 6/09/23 → 8/09/23 |
Internet address |