Support for national measures against the COVID-19 pandemic can be measured by collecting responses to questionnaires. In this paper we explore a less costly and less time-consuming method: by analyzing social media data. We compare stances regarding anti-pandemic measures extracted from tweets with the questionnaire results provided by the Dutch national health institute RIVM. We find similarities and differences and discuss the results.

With the growing abundance of unlabeled data in real-world tasks, researchers have to rely on the predictions given by black-boxed computational models. However, it is an often neglected fact that these models may be scoring high on accuracy for the wrong reasons. In this paper, we present a practical impact analysis of enabling model transparency by various presentation forms. For this purpose, we developed an environment that empowers non-computer scientists to become practicing data scientists in their own research field. We demonstrate the gradually increasing understanding of journalism historians through a real-world use case study on automatic genre classification of newspaper articles. This study is a first step towards trusted usage of machine learning pipelines in a responsible way.

We study the discursive practices of politicians and journalists on social media. For this we need more annotated data than we currently have but the annotation process is time-consuming and costly. In this paper we examine machine learning methods for automatically annotating unseen tweetsbased on a small set of manually annotated tweets. Forimproving the performance of the learner, we focus onmethods related to training data expansion, like artificialtraining data, active learning and incorporating languagemodels developed from unannotated text.

Lexical taxonomies, such as WordNet, are important resources underlying natural language processing techniques such as machine translation and word-sense disambiguation. However, creating and maintaining such taxonomies manually is a tedious and time-consuming task. This has led to a great deal of interest in automatic methods for retrieving taxonomic relations. Efforts for both manual development of taxonomies and automatic acquisition methods have largely focused on English-language resources. Although WordNets exist for many languages, these are usually much smaller than Princeton WordNet (PWN) [1], the major semantic resource for English. For English, an excellent method has been developed for automatically extending lexical taxonomies. Snow et al. [4] show that it is possible to predict new and precise hypernymhyponym relations from a parsed corpus. Unfortunately, the method cannot be easily applied to other languages, as it relies on existing lexical resources. First, the authors use the large amount of data already contained in the PWN to extract a pattern lexicon and train their hypernym classifier. Second, the authors apply word-sense disambiguation based on an existing sense-tagged corpus. Problems in transferring the method to another language include the small size of non-English WordNets and the lack of sense-tagged corpora in other languages. In this paper we apply the method of [4] to a non-English language (Dutch) for which only a basic WordNet and a parser are available. We find that, without an additional sense-tagged corpus, this approach is highly susceptible to noise due to word sense ambiguity. We propose and evaluate two methods to address this problem.