In this paper we present multiple approaches for event detection on document and sentence level, as well as a technique for event sentence co-reference resolution. The advantage of our co-reference resolution approach, which handles the task as a clustering problem, is that we use a single neural net to solve the task, which stands in contrast to other clustering algorithms that often are build on more complex models. This means that we can set our focus on the optimization of a single neural network instead of having to optimize numerous different parameters. We use small densely connected neural networks and pre-trained multilingual transformer embeddings in all subtasks. We use either document or sentence embeddings, depending on the task, and refrain from using word embeddings, so that the implementation of complicated network structures and unfolding of RNNs, which can deal with input of different sizes, is not necessary. We achieved an average macro F1 of 0.65 in subtask 1 (i.e., document level classification), and a macro F1 of 0.70 in subtask 2 (i.e., sentence level classification). For the co-reference resolution subtask, we achieved an average CoNLL-2012 score across all languages of 0.83.

We present our submission to Task 2 of the Socio-political and Crisis Events Detection Shared Task at the CASE @ ACL-IJCNLP 2021 workshop. The task at hand aims at the fine-grained classification of socio-political events. Our best model was a fine-tuned RoBERTa transformer model using document embeddings. The corpus consisted of a balanced selection of sub-events extracted from the ACLED event dataset. We achieved a macro F-score of 0.923 and a micro F-score of 0.932 during our preliminary experiments on a held-out test set. The same model also performed best on the shared task test data (weighted F-score = 0.83). To analyze the results we calculated the topic compactness of the commonly misclassified events and conducted an error analysis.

The information space, where information is generated, stored, exchanged and discussed, is not idyllic but a space where campaigns of disinformation and destabilization are conducted. Such campaigns are subsumed under the terms “hybrid warfare” and “information warfare” (Woolley and Howard, 2017). In order to enable awareness of them, we propose an information state dashboard comprising various components/apps for data collection, analysis and visualization. The aim of the dashboard is to support an analyst in generating a common operational picture of the information space, link it with an operational picture of the physical space and, thus, contribute to overarching situational awareness. The dashboard is work in progress. However, a first prototype with components for exploiting elementary language statistics, keyword and metadata analysis, text classification and network analysis has been implemented. Further components, in particular, for event extraction and sentiment analysis are under development. As a demonstration case, we briefly discuss the analysis of historical data regarding violent anti-migrant protests and respective counter-protests that took place in Chemnitz in 2018.