Olaf Zukunft


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Forum 4.0: An Open-Source User Comment Analysis Framework
Marlo Haering | Jakob Smedegaard Andersen | Chris Biemann | Wiebke Loosen | Benjamin Milde | Tim Pietz | Christian Stöcker | Gregor Wiedemann | Olaf Zukunft | Walid Maalej
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations

With the increasing number of user comments in diverse domains, including comments on online journalism and e-commerce websites, the manual content analysis of these comments becomes time-consuming and challenging. However, research showed that user comments contain useful information for different domain experts, which is thus worth finding and utilizing. This paper introduces Forum 4.0, an open-source framework to semi-automatically analyze, aggregate, and visualize user comments based on labels defined by domain experts. We demonstrate the applicability of Forum 4.0 with comments analytics scenarios within the domains of online journalism and app stores. We outline the underlying container architecture, including the web-based user interface, the machine learning component, and the task manager for time-consuming tasks. We finally conduct machine learning experiments with simulated annotations and different sampling strategies on existing datasets from both domains to evaluate Forum 4.0’s performance. Forum 4.0 achieves promising classification results (ROC-AUC ≥ 0.9 with 100 annotated samples), utilizing transformer-based embeddings with a lightweight logistic regression model. We explain how Forum 4.0’s architecture is applicable for millions of user comments in real-time, yet at feasible training and classification costs.

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REM: Efficient Semi-Automated Real-Time Moderation of Online Forums
Jakob Smedegaard Andersen | Olaf Zukunft | Walid Maalej
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations

This paper presents REM, a novel tool for the semi-automated real-time moderation of large scale online forums. The growing demand for online participation and the increasing number of user comments raise challenges in filtering out harmful and undesirable content from public debates in online forums. Since a manual moderation does not scale well and pure automated approaches often lack the required level of accuracy, we suggest a semi-automated moderation approach. Our approach maximizes the efficiency of manual efforts by targeting only those comments for which human intervention is needed, e.g. due to high classification uncertainty. Our tool offers a rich visual interactive environment enabling the exploration of online debates. We conduct a preliminary evaluation experiment to demonstrate the suitability of our approach and publicly release the source code of REM.