Can Analytics as a Service Save the Online Discussion Culture? – The Case of Comment Moderation in the Media Industry

Brunk, Jens; Niemann, Marco; Riehle, Dennis M

Abstract

In recent years, online public discussions face a proliferation of racist, politically, and religiously motivated hate comments, threats, and insults. With the failure of purely manual moderation, platform operators started searching for semi-automated or even completely automated approaches for comment moderation. One promising option to (semi-) automate the moderation process is the application of Natural Language Processing and Machine Learning (ML) techniques. In this paper we describe the challenges, that currently prevent the application of these techniques and therefore the development of (semi-) and automated solutions. As most of the challenges (e.g., curation of big datasets) require huge financial investments, only big players, such as Google or Facebook, will be able to invest in them. Many of the smaller and medium-sized internet companies will fall behind. To allow this bulk of (media) companies to stay competitive, we design a novel Analytics as a Service (AaaS) offering that will also allow small and medium sized enterprises to profit from ML decision support. We then use the identified challenges to evaluate the conceptual design of the business model and highlight areas of future research to enable the instantiation of the AaaS platform.

Keywords
comment moderation; machine learning; hate speech; abusive language; moderation; business model