Incremental Machine Learning for Text Classification in Comment Moderation Systems

Wolters, Anna; Müller, Kilian; Riehle, Dennis Maximilian


Abstract

Over the last decade, researchers presented (semi-)automated comment moderation systems (CMS) based on machine learning (ML) and natural language processing (NLP) techniques to support the identification of hateful and offensive comments in online discussion forums. A common challenge in providing and operating comment moderation systems is the dynamic nature of language. As language evolves over time, continuous performance evaluations and resource-inefficient model retraining are applied to ensure high-quality identification of hate speech in the long-term use of comment moderation systems. To study the potentials of adaptable machine learning models embedded in comment moderation systems, we present an incremental machine learning approach for semi-automated comment moderation systems. This study shows a comparison of incrementally-trained ML models and batch-trained ML models used in comment moderation systems.

Keywords
Incremental Learning; Text Classification; Comment Moderation Systems



Publication type
Forschungsartikel in Sammelband (Konferenz)

Peer reviewed
Yes

Publication status
Published

Year
2022

Conference
4th Multidisciplinary International Symposium on Disinformation in Open Online Media

Venue
Boise, ID

Book title
Disinformation in Open Online Media - 4th Multidisciplinary International Symposium, MISDOOM 2022, Boise, ID, USA, October 11–12, 2022, Proceedings

Editor
Spezzano, Francesca; Amaral, Adriana; Ceolin, Davide; Fazio, Lisa; Serra, Edoardo

Start page
138

End page
153

Volume
13545

Title of series
Lecture Notes in Computer Science

Publisher
Springer Nature

Place
Cham

Language
English

ISSN
0302-9743

ISBN
978-3-031-18252-5

DOI