Publications of the involved scientists
2021 |
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2. | Mina Schütz; Alexander Schindler; Melanie Siegel; Kawa Nazemi Automatic Fake News Detection with Pre-trained Transformer Models Proceedings Article In: Alberto Del Bimbo; Rita Cucchiara; Stan Sclaroff; Giovanni Maria Farinella; Tao Mei; Marco Bertini; Hugo Jair Escalante; Roberto Vezzani (Ed.): Pattern Recognition. ICPR International Workshops and Challenges, pp. 627–641, Springer International Publishing, Cham, 2021, ISBN: 978-3-030-68787-8. @inproceedings{10.1007/978-3-030-68787-8_45, The automatic detection of disinformation and misinformation has gained attention during the last years, since fake news has a critical impact on democracy, society, and journalism and digital literacy. In this paper, we present a binary content-based classification approach for detecting fake news automatically, with several recently published pre-trained language models based on the Transformer architecture. The experiments were conducted on the FakeNewsNet dataset with XLNet, BERT, RoBERTa, DistilBERT, and ALBERT and various combinations of hyperparameters. Different preprocessing steps were carried out with only using the body text, the titles and a concatenation of both. It is concluded that Transformers are a promising approach to detect fake news, since they achieve notable results, even without using a large dataset. Our main contribution is the enhancement of fake news' detection accuracy through different models and parametrizations with a reproducible result examination through the conducted experiments. The evaluation shows that already short texts are enough to attain 85% accuracy on the test set. Using the body text and a concatenation of both reach up to 87% accuracy. Lastly, we show that various preprocessing steps, such as removing outliers, do not have a significant impact on the models prediction output. |
2015 |
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1. | Dirk Burkhardt; Kawa Nazemi; Silvana Tomic; Egils Ginters Best-practice Piloting of Integrated Social Media Analysis Solution for E-Participation in Cities Journal Article In: Procedia Computer Science, vol. 77, pp. 11 - 21, 2015, ISSN: 1877-0509, (ICTE in regional Development 2015 Valmiera, Latvia). @article{Burkhardt2015b, Goal definitions and developments are challenging in large-scale projects, because of the different expertise and skills of the stakeholders. Development often fails its intended goal because of misunderstandings and unclear definitions and descriptions during the planning phase. The paper describes a novel approach to collecting requirements and defining development plans by provisioning a guideline which informs what has to be done, when and in what form. The User Case Requirement Analysis model was applied in the large-scale European project FUPOL during the development of a Social Media Analysis System. Based on this a successful task-based evaluation could be performed that shows the benefit of the model and the software. |