Jörg Becker

Lennart Justen, Kilian Müller, Marco Niemann et al.

The spread of online hate has become a significant problem for newspapers that host comment sections. As a result, there is growing interest in using machine learning and natural language processing for (semi-) automated abusive language detection to avoid manual comment moderation costs or having to shut down comment sections altogether. However, much of the past work on abusive language detection assumes that classifiers operate in a static language environment, despite language and news being in a state of constant flux. In this paper, we show using a new German newspaper comments dataset that the classifiers trained with naive ML techniques like a random-test train split will underperform on future data, and that a time stratified evaluation split is more appropriate. We also show that classifier performance rapidly degrades when evaluated on data from a different period than the training data. Our findings suggest that it is necessary to consider the temporal dynamics of language when developing an abusive language detection system or risk deploying a model that will quickly become defunct.

Sven Weinzierl, Sandra Zilker, Jens Brunk et al.

Predictive business process monitoring (PBPM) is a class of techniques designed to predict behaviour, such as next activities, in running traces. PBPM techniques aim to improve process performance by providing predictions to process analysts, supporting them in their decision making. However, the PBPM techniques` limited predictive quality was considered as the essential obstacle for establishing such techniques in practice. With the use of deep neural networks (DNNs), the techniques` predictive quality could be improved for tasks like the next activity prediction. While DNNs achieve a promising predictive quality, they still lack comprehensibility due to their hierarchical approach of learning representations. Nevertheless, process analysts need to comprehend the cause of a prediction to identify intervention mechanisms that might affect the decision making to secure process performance. In this paper, we propose XNAP, the first explainable, DNN-based PBPM technique for the next activity prediction. XNAP integrates a layer-wise relevance propagation method from the field of explainable artificial intelligence to make predictions of a long short-term memory DNN explainable by providing relevance values for activities. We show the benefit of our approach through two real-life event logs.

S. Weinzierl, S. Zilker, J. Brunk et al.

Researchers have proposed a variety of predictive business process monitoring (PBPM) techniques aiming to predict future process behaviour during the process execution. Especially, techniques for the next activity prediction anticipate great potential in improving operational business processes. To gain more accurate predictions, a plethora of these techniques rely on deep neural networks (DNNs) and consider information about the context, in which the process is running. However, an in-depth comparison of such techniques is missing in the PBPM literature, which prevents researchers and practitioners from selecting the best solution for a given event log. To remedy this problem, we empirically evaluate the predictive quality of three promising DNN architectures, combined with five proven encoding techniques and based on five context-enriched real-life event logs. We provide four findings that can support researchers and practitioners in designing novel PBPM techniques for predicting the next activities.