1 code implementation • Findings (EMNLP) 2021 • Timo Spinde, Manuel Plank, Jan-David Krieger, Terry Ruas, Bela Gipp, Akiko Aizawa
Fine-tuning and evaluating the model on our proposed supervised data set, we achieve a macro F1-score of 0. 804, outperforming existing methods.
no code implementations • 27 Feb 2024 • Tomáš Horych, Martin Wessel, Jan Philip Wahle, Terry Ruas, Jerome Waßmuth, André Greiner-Petter, Akiko Aizawa, Bela Gipp, Timo Spinde
MAGPIE confirms that MTL is a promising approach for addressing media bias detection, enhancing the accuracy and efficiency of existing models.
1 code implementation • 26 Dec 2023 • Timo Spinde, Smi Hinterreiter, Fabian Haak, Terry Ruas, Helge Giese, Norman Meuschke, Bela Gipp
However, we have identified a lack of interdisciplinarity in existing projects, and a need for more awareness of the various types of media bias to support methodologically thorough performance evaluations of media bias detection systems.
1 code implementation • 25 Apr 2023 • Martin Wessel, Tomáš Horych, Terry Ruas, Akiko Aizawa, Bela Gipp, Timo Spinde
A unified benchmark encourages the development of more robust systems and shifts the current paradigm in media bias detection evaluation towards solutions that tackle not one but multiple media bias types simultaneously.
no code implementations • 17 Mar 2023 • Norman Meuschke, Apurva Jagdale, Timo Spinde, Jelena Mitrović, Bela Gipp
Using the new framework, we benchmark ten freely available tools in extracting document metadata, bibliographic references, tables, and other content elements from academic PDF documents.
no code implementations • 7 Nov 2022 • Timo Spinde, Jan-David Krieger, Terry Ruas, Jelena Mitrović, Franz Götz-Hahn, Akiko Aizawa, Bela Gipp
Media has a substantial impact on the public perception of events.
1 code implementation • 29 Sep 2022 • Timo Spinde, Manuel Plank, Jan-David Krieger, Terry Ruas, Bela Gipp, Akiko Aizawa
Fine-tuning and evaluating the model on our proposed supervised data set, we achieve a macro F1-score of 0. 804, outperforming existing methods.
1 code implementation • 22 May 2022 • Jan-David Krieger, Timo Spinde, Terry Ruas, Juhi Kulshrestha, Bela Gipp
We present DA-RoBERTa, a new state-of-the-art transformer-based model adapted to the media bias domain which identifies sentence-level bias with an F1 score of 0. 814.
no code implementations • 26 Dec 2021 • Timo Spinde
My vision is to devise a system that helps news readers become aware of media coverage differences caused by bias.
no code implementations • 14 Dec 2021 • Timo Spinde, David Krieger, Manuel Plank, Bela Gipp
Our results demonstrate the existing crowdsourcing approaches' lack of data quality, underlining the need for a trained expert framework to gather a more reliable dataset.
no code implementations • 14 Dec 2021 • Timo Spinde, Lada Rudnitckaia, Felix Hamborg, Bela Gipp
The underlying idea is that the context of biased words in different news outlets varies more strongly than the one of non-biased words, since the perception of a word as being biased differs depending on its context.
no code implementations • 14 Dec 2021 • Timo Spinde, Christina Kreuter, Wolfgang Gaissmaier, Felix Hamborg, Bela Gipp, Helge Giese
To name an example: Intending to measure bias in a news article, should we ask, "How biased is the article?"
no code implementations • 14 Dec 2021 • Timo Spinde, Kanishka Sinha, Norman Meuschke, Bela Gipp
We present a free and open-source tool for creating web-based surveys that include text annotation tasks.
no code implementations • 18 Oct 2021 • Felix Hamborg, Timo Spinde, Kim Heinser, Karsten Donnay, Bela Gipp
We present an in-progress system for news recommendation that is the first to automate the manual procedure of content analysis to reveal person-targeting biases in news articles reporting on policy issues.