Moderating the Good, the Bad, and the Hateful: Moderators' Attitudes towards ML-based Comment Moderation Support Systems

Koelmann, Holger; Müller, Kilian; Niemann, Marco; Riehle, Dennis Maximilian


Abstract

Comment sections have established themselves as essential elements of the public discourse.
However, they put considerable pressure on the hosting organizations to keep them clean of hateful and abusive comments. This is necessary to prevent violating legal regulations and to avoid appalling their readers.
With commenting being a typically free feature and anonymity encouraging increasingly daunting comments, many newspapers struggle to operate economically viable comment sections.
Hence, throughout the last decade, researchers set forth to develop machine learning (ML) models to automate this work. With increasingly sophisticated algorithms, research is starting on comment moderation support systems that integrate ML models to relieve moderators from parts of their workload.
Our research sets forth to assess the attitudes of moderators towards such systems to provide guidance for future developments. This paper presents the findings from three conducted expert interviews, which also included tool usage observations.

Keywords
Community Management; Machine Learning; Content Moderation; Comment Moderation Support System; Digital Work



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
100

End page
113

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