In his thesis, Irtaza Rasheed implementated a universal name disambiguation approach that considers almost any existing property to identify authors. After an author of a paper is identied, the normalized name writing form on the paper is used to refine the author model and even give an overview about the different writing forms of the author’s name. This can be achieved by first examine the research on Human-Computer Interaction specifically with focus on (Visual) Trend Analysis. Furthermore, a research on different name disambiguation techniques. After that, building a concept and implementing a generalized method to identify author name and affiliation disambiguation while evaluating different properties.
Mina Schütz wrote her master thesis under the supervision of Prof. Dr. Kawa Nazemi and Prof. Dr. Melanie Siegel toward “Detection and Identification of Fake News: Binary Content Classification with a Pre-Trained Language Model” at Austrian Institute of Technology (AIT), Center for Digital Safety & Security in Vienna. During her work, she could successfully conceptualize and develop a system that enables the classification of news with a high probability as fake or real on the basis of Artificial Intelligence algorithm. The work is a great product of scientific and empirical acting and will be published in the scientific world soon.
For her excellent work and the high societal relevance, the impact magazine of the h_da has interviewed her to outline the topic and her approach in a better understandable manner, particularly for non-computer-science experts. The full interview is available (in German only) on: https://impact.h-da.de/forschung/corona-und-fakenews/.