Context-Aware Content Moderation for German Newspaper Comments
This work addresses a gap in automated moderation for German-language newspaper forums, which is an incremental advance as it applies existing methods to a less-studied domain with added context.
The paper tackled content moderation for German newspaper comments by developing binary classification models that incorporate contextual information like user history and article themes, finding that CNN and LSTM models with context performed competitively with state-of-the-art methods, while ChatGPT-3.5 Turbo underperformed without improvement from context.
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.