Incremental Machine Learning for Text Classification in Comment Moderation Systems
Wolters, Anna; Müller, Kilian; Riehle, Dennis Maximilian
Zusammenfassung
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.
Schlüsselwörter
Incremental Learning; Text Classification; Comment Moderation Systems
Zitieren als
Wolters, A., Müller, K., & Riehle, D. M. (2022). Incremental Machine Learning for Text Classification in Comment Moderation Systems. In Spezzano, F., Amaral, A., Ceolin, D., Fazio, L., & Serra, E. (Eds.),
Disinformation in Open Online Media — 4th Multidisciplinary International Symposium, MISDOOM 2022, Boise, ID, USA, October 11–12, 2022, Proceedings (pp. 138–153). Lecture Notes in Computer Science: Vol. 13545. Cham: Springer Nature.
Details
Publikationstyp
Forschungsartikel in Sammelband (Konferenz)
Begutachtet
Ja
Publikationsstatus
Veröffentlicht
Jahr
2022
Konferenz
4th Multidisciplinary International Symposium on Disinformation in Open Online Media
Konferenzort
Boise, ID
Buchtitel
Disinformation in Open Online Media - 4th Multidisciplinary International Symposium, MISDOOM 2022, Boise, ID, USA, October 11–12, 2022, Proceedings
Herausgeber
Spezzano, Francesca; Amaral, Adriana; Ceolin, Davide; Fazio, Lisa; Serra, Edoardo
Erste Seite
138
Letzte Seite
153
Band
13545
Reihe
Lecture Notes in Computer Science
Verlag
Springer Nature
Ort
Cham
Sprache
Englisch
ISSN
0302-9743
ISBN
978-3-031-18252-5
DOI
