Context-Aware Content Moderation for German Newspaper Comments

TL;DR

This paper develops context-aware models for automatic content moderation in German newspaper comments, demonstrating that CNN and LSTM models benefit from context while ChatGPT-3.5 Turbo does not.

Contribution

It introduces and evaluates context-aware classification models specifically tailored for German newspaper comment moderation, filling a research gap.

Findings

CNN and LSTM models improve with context

ChatGPT-3.5 Turbo's zero-shot classification does not benefit from context

Context-aware models perform competitively with state-of-the-art methods

Abstract

The increasing volume of online discussions requires advanced automatic content moderation to maintain responsible discourse. While hate speech detection on social media is well-studied, research on German-language newspaper forums remains limited. Existing studies often neglect platform-specific context, such as user history and article themes. This paper addresses this gap by developing and evaluating binary classification models for automatic content moderation in German newspaper forums, incorporating contextual information. Using LSTM, CNN, and ChatGPT-3.5 Turbo, and leveraging the One Million Posts Corpus from the Austrian newspaper Der Standard, we assess the impact of context-aware models. Results show that CNN and LSTM models benefit from contextual information and perform competitively with state-of-the-art approaches. In contrast, ChatGPT's zero-shot classification does not improve with added context and underperforms.