Disinformation has become increasingly relevant in recent years both as a political issue and as object of research. Datasets for training machine learning models, especially for other languages than English, are sparse and the creation costly. Annotated datasets often have only binary or multiclass labels, which provide little information about the grounds and system of such classifications. We propose a novel textual dataset GerDISDETECT for German disinformation. To provide comprehensive analytical insights, a fine-grained taxonomy guided annotation scheme is required. The goal of this dataset, instead of providing a direct assessment regarding true or false, is to provide wide-ranging semantic descriptors that allow for complex interpretation as well as inferred decision-making regarding information and trustworthiness of potentially critical articles. This allows this dataset to be also used for other tasks. The dataset was collected in the first three months of 2022 and contains 39 multilabel classes with 5 top-level categories for a total of 1,890 articles: General View (3 labels), Offensive Language (11 labels), Reporting Style (15 labels), Writing Style (6 labels), and Extremism (4 labels). As a baseline, we further pre-trained a multilingual XLM-R model on around 200,000 unlabeled news articles and fine-tuned it for each category.

In this work, we present a new publicly available offensive language dataset of 10.278 German social media comments collected in the first half of 2021 that were annotated by in total six annotators. With twelve different annotation categories, it is far more comprehensive than other datasets, and goes beyond just hate speech detection. The labels aim in particular also at toxicity, criminal relevance and discrimination types of comments. Furthermore, about half of the comments are from coherent parts of conversations, which opens the possibility to consider the comments’ contexts and do conversation analyses in order to research the contagion of offensive language in conversations.

In this work, we present our approaches on the toxic comment classification task (subtask 1) of the GermEval 2021 Shared Task. For this binary task, we propose three models: a German BERT transformer model; a multilayer perceptron, which was first trained in parallel on textual input and 14 additional linguistic features and then concatenated in an additional layer; and a multilayer perceptron with both feature types as input. We enhanced our pre-trained transformer model by re-training it with over 1 million tweets and fine-tuned it on two additional German datasets of similar tasks. The embeddings of the final fine-tuned German BERT were taken as the textual input features for our neural networks. Our best models on the validation data were both neural networks, however our enhanced German BERT gained with a F1-score = 0.5895 a higher prediction on the test data.

Spreading ones opinion on the internet is becoming more and more important. A problem is that in many discussions people often argue with supposed facts. This year’s GermEval 2021 focuses on this topic by incorporating a shared task on the identification of fact-claiming comments. This paper presents the contribution of the AIT FHSTP team at the GermEval 2021 benchmark for task 3: “identifying fact-claiming comments in social media texts”. Our methodological approaches are based on transformers and incorporate 3 different models: multilingual BERT, GottBERT and XML-RoBERTa. To solve the fact claiming task, we fine-tuned these transformers with external data and the data provided by the GermEval task organizers. Our multilingual BERT model achieved a precision-score of 72.71%, a recall of 72.96% and an F1-Score of 72.84% on the GermEval test set. Our fine-tuned XML-RoBERTa model achieved a precision-score of 68.45%, a recall of 70.11% and a F1-Score of 69.27%. Our best model is GottBERT (i.e., a BERT transformer pre-trained on German texts) fine-tuned on the GermEval 2021 data. This transformer achieved a precision of 74.13%, a recall of 75.11% and an F1-Score of 74.62% on the test set.