This paper deals with the question of how artificial intelligence can be used to detect media bias in the overarching topic of manipulation and mood-making. We show three fields of actions that result from using machine learning to analyze media bias: the evaluation principles of media bias, the information presentation of media bias, and the transparency of media bias evaluation. Practical applications of our research results arise in the professional environment for journalists and publishers, as well as in the everyday life of citizens. First, automated analysis could be used to analyze text in real-time and promote balanced coverage in reporting. Second, an intuitive web browser application could reveal existing bias in news texts in a way that citizens can understand. Finally, in education, pupils can experience media bias and the use of artificial intelligence in practice, fostering their media literacy.

Web hosting companies strive to provide customised customer services and want to know the commercial intent of a website. Whether a website is run by an individual person, a company, a non-profit organisation, or a public institution constitutes a great challenge in website classification as website content might be sparse. In this paper, we present a novel approach for determining the commercial intent of websites by using both supervised and unsupervised machine learning algorithms. Based on a large real-world data set, we evaluate our model with respect to its effectiveness and efficiency and observe the best performance with a multilayer perceptron.

In recent years, several noteworthy large, cross-domain, and openly available knowledge graphs (KGs) have been created. These include DBpedia, Freebase, OpenCyc, Wikidata, and YAGO. Although extensively in use, these KGs have not been subject to an in-depth comparison so far. In this survey, we provide data quality criteria according to which KGs can be analyzed and analyze and compare the above mentioned KGs. Furthermore, we propose a framework for finding the most suitable KG for a given setting.