Work Experience
Guest Researcher @ National Institute of Informatics Tokyo
2025 (3 months)
2022/23 (6 months)
Researcher @ Media Bias Research Group
since 2021 (4 years)
Research Assistant @ Fraunhofer IFF
2020 – 2021 (1 year 4 months)
Research Assistant @ Honda R&D Europe
2018 – 2019 (1 year 6 months)
Developer @ Core Learning Production
2017 – 2018 (1 year)
Education
Ph.D. Student @ University of Würzburg
since 2022
Master of Arts Interaction Design @ University of Applied Science Magdeburg (1,1)
2020 – 2021 (1 year 6 months)
Bachelor of Arts Interactive Media Design @ University of Applied Science Darmstadt (1,7)
2015 – 2019 (4 years)
Scholarships
Ph.D. Scholarship Hanns-Seidel-Foundation (3 years)
DAAD IFI Scholarship (2x)
Publications
2025
Hinterreiter, Smi; Wessel, Martin; Schliski, Fabian; Echizen, Isao; Latoschik, Marc Erich; Spinde, Timo
NewsUnfold: Creating a News-Reading Application That Indicates Linguistic Media Bias and Collects Feedback Proceedings Article
In: Proceedings of the international AAAI conference on web and social media (ICWSM'25), AAAI, Copenhagen, Denmark, 2025.
@inproceedings{Hinterreiter2025NewsUnfold,
title = {NewsUnfold: Creating a News-Reading Application That Indicates Linguistic Media Bias and Collects Feedback},
author = {Smi Hinterreiter and Martin Wessel and Fabian Schliski and Isao Echizen and Marc Erich Latoschik and Timo Spinde},
year = {2025},
date = {2025-06-01},
urldate = {2025-06-01},
booktitle = {Proceedings of the international AAAI conference on web and social media (ICWSM'25)},
volume = {19},
publisher = {AAAI},
address = {Copenhagen, Denmark},
abstract = {Media bias is a multifaceted problem, leading to one-sided views and impacting decision-making. A way to address digital media bias is to detect and indicate it automatically through machine-learning methods. However, such detection is limited due to the difficulty of obtaining reliable training data. Human-in-the-loop-based feedback mechanisms have proven an effective way to facilitate the data-gathering process. Therefore, we introduce and test feedback mechanisms for the media bias domain, which we then implement on NewsUnfold, a news-reading web application to collect reader feedback on machine-generated bias highlights within online news articles. Our approach augments dataset quality by significantly increasing inter-annotator agreement by 26.31},
key = {Hinterreiter2025NewsUnfold},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Media bias is a multifaceted problem, leading to one-sided views and impacting decision-making. A way to address digital media bias is to detect and indicate it automatically through machine-learning methods. However, such detection is limited due to the difficulty of obtaining reliable training data. Human-in-the-loop-based feedback mechanisms have proven an effective way to facilitate the data-gathering process. Therefore, we introduce and test feedback mechanisms for the media bias domain, which we then implement on NewsUnfold, a news-reading web application to collect reader feedback on machine-generated bias highlights within online news articles. Our approach augments dataset quality by significantly increasing inter-annotator agreement by 26.31
Spinde, Timo; Lin, Luyang; Hinterreiter, Smi; Echizen, Isao
Leveraging Large Language Models for Automated Definition Extraction with TaxoMatic A Case Study on Media Bias Proceedings Article
In: Proceedings of the international AAAI conference on web and social media (ICWSM'25: Datasets), AAAI, Copenhagen, Denmark, 2025.
@inproceedings{Spinde2025Leveraging,
title = {Leveraging Large Language Models for Automated Definition Extraction with TaxoMatic A Case Study on Media Bias},
author = {Timo Spinde and Luyang Lin and Smi Hinterreiter and Isao Echizen},
year = {2025},
date = {2025-06-01},
urldate = {2025-06-01},
booktitle = {Proceedings of the international AAAI conference on web and social media (ICWSM'25: Datasets)},
volume = {19},
publisher = {AAAI},
address = {Copenhagen, Denmark},
abstract = {This paper introduces TaxoMatic, a framework that leverages large language models to automate definition extraction from academic literature. Focusing on the media bias domain, the framework encompasses data collection, LLM-based relevance classification, and extraction of conceptual definitions. Evaluated on a dataset of 2,398 manually rated articles, the study demonstrates the frameworks effectiveness, with Claude-3-sonnet achieving the best results in both relevance classification and definition extraction. Future directions include expanding datasets and applying TaxoMatic to additional domains.},
key = {Spinde2025 Leveraging},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper introduces TaxoMatic, a framework that leverages large language models to automate definition extraction from academic literature. Focusing on the media bias domain, the framework encompasses data collection, LLM-based relevance classification, and extraction of conceptual definitions. Evaluated on a dataset of 2,398 manually rated articles, the study demonstrates the frameworks effectiveness, with Claude-3-sonnet achieving the best results in both relevance classification and definition extraction. Future directions include expanding datasets and applying TaxoMatic to additional domains.
2024
Hinterreiter, Smi; Spinde, Timo; Oberdörfer, Sebastian; Echizen, Isao; Latoschik, Marc Erich
News Ninja: Gamified Annotation of Linguistic Bias in Online News Journal Article
In: Proc. ACM Hum.-Comput. Interact., vol. 8, no. CHI PLAY, 2024, (Place: New York, NY, USA Publisher: Association for Computing Machinery tex.articleno: 327).
@article{Hinterreiter2024News,
title = {News Ninja: Gamified Annotation of Linguistic Bias in Online News},
author = {Smi Hinterreiter and Timo Spinde and Sebastian Oberdörfer and Isao Echizen and Marc Erich Latoschik},
doi = {10.1145/3677092},
year = {2024},
date = {2024-10-01},
urldate = {2024-10-01},
journal = {Proc. ACM Hum.-Comput. Interact.},
volume = {8},
number = {CHI PLAY},
abstract = {Recent research shows that visualizing linguistic bias mitigates its negative effects. However, reliable automatic detection methods to generate such visualizations require costly, knowledge-intensive training data. To facilitate data collection for media bias datasets, we present News Ninja, a game employing data-collecting game mechanics to generate a crowdsourced dataset. Before annotating sentences, players are educated on media bias via a tutorial. Our findings show that datasets gathered with crowdsourced workers trained on News Ninja can reach significantly higher inter-annotator agreements than expert and crowdsourced datasets with similar data quality. As News Ninja encourages continuous play, it allows datasets to adapt to the reception and contextualization of news over time, presenting a promising strategy to reduce data collection expenses, educate players, and promote long-term bias mitigation.},
key = {Hinterreiter2024News},
note = {Place: New York, NY, USA
Publisher: Association for Computing Machinery
tex.articleno: 327},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Recent research shows that visualizing linguistic bias mitigates its negative effects. However, reliable automatic detection methods to generate such visualizations require costly, knowledge-intensive training data. To facilitate data collection for media bias datasets, we present News Ninja, a game employing data-collecting game mechanics to generate a crowdsourced dataset. Before annotating sentences, players are educated on media bias via a tutorial. Our findings show that datasets gathered with crowdsourced workers trained on News Ninja can reach significantly higher inter-annotator agreements than expert and crowdsourced datasets with similar data quality. As News Ninja encourages continuous play, it allows datasets to adapt to the reception and contextualization of news over time, presenting a promising strategy to reduce data collection expenses, educate players, and promote long-term bias mitigation.
Spinde, Timo; Hinterreiter, Smi; Haak, Fabian; Ruas, Terry; Giese, Helge; Meuschke, Norman; Gipp, Bela
The Media Bias Taxonomy: A Systematic Literature Review on the Forms and Automated Detection of Media Bias Miscellaneous
2024.
@misc{spinde2024mediabiastaxonomysystematic,
title = {The Media Bias Taxonomy: A Systematic Literature Review on the Forms and Automated Detection of Media Bias},
author = {Timo Spinde and Smi Hinterreiter and Fabian Haak and Terry Ruas and Helge Giese and Norman Meuschke and Bela Gipp},
url = {https://arxiv.org/abs/2312.16148},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
abstract = {The way the media presents events can significantly affect public perception, which in turn can alter people's beliefs and views. Media bias describes a one-sided or polarizing perspective on a topic. This article summarizes the research on computational methods to detect media bias by systematically reviewing 3140 research papers published between 2019 and 2022. To structure our review and support a mutual understanding of bias across research domains, we introduce the Media Bias Taxonomy, which provides a coherent overview of the current state of research on media bias from different perspectives. We show that media bias detection is a highly active research field, in which transformer-based classification approaches have led to significant improvements in recent years. These improvements include higher classification accuracy and the ability to detect more fine-granular types of bias. 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. Concluding from our analysis, we see the integration of recent machine learning advancements with reliable and diverse bias assessment strategies from other research areas as the most promising area for future research contributions in the field.},
key = {spinde2024mediabiastaxonomysystematic},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
The way the media presents events can significantly affect public perception, which in turn can alter people's beliefs and views. Media bias describes a one-sided or polarizing perspective on a topic. This article summarizes the research on computational methods to detect media bias by systematically reviewing 3140 research papers published between 2019 and 2022. To structure our review and support a mutual understanding of bias across research domains, we introduce the Media Bias Taxonomy, which provides a coherent overview of the current state of research on media bias from different perspectives. We show that media bias detection is a highly active research field, in which transformer-based classification approaches have led to significant improvements in recent years. These improvements include higher classification accuracy and the ability to detect more fine-granular types of bias. 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. Concluding from our analysis, we see the integration of recent machine learning advancements with reliable and diverse bias assessment strategies from other research areas as the most promising area for future research contributions in the field.
2021
Hinterreiter, Smilla
A Gamified Approach To Automatically Detect Biased Wording And Train Critical Reading Proceedings Article
In: 2021 IEEE International Conference on Data Mining Workshops (ICDMW), 2021.
@inproceedings{hinterreiterGamifiedApproachAutomatically2021,
title = {A Gamified Approach To Automatically Detect Biased Wording And Train Critical Reading},
author = {Smilla Hinterreiter},
url = {https://media-bias-research.org/wp-content/uploads/2021/10/hinterreiter2021a.pdf},
doi = {10.1109/ICDMW53433.2021.00141},
year = {2021},
date = {2021-10-01},
urldate = {2021-10-01},
booktitle = {2021 IEEE International Conference on Data Mining Workshops (ICDMW)},
abstract = {Biased media has an effect on the public perception of occurring events. By altering word choice, outlets can alter beliefs and views. A gold standard data set is needed to train sufficient classifiers that detect biased wording. This work aims to develop a game that trains players to read news critically while collecting their annotations. The vision is to tackle the complex problem of media bias detection with a very scalable, high quality, and gold standard data set to overcome the drawbacks of current models in the area.},
key = {hinterreiterGamifiedApproachAutomatically2021},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Biased media has an effect on the public perception of occurring events. By altering word choice, outlets can alter beliefs and views. A gold standard data set is needed to train sufficient classifiers that detect biased wording. This work aims to develop a game that trains players to read news critically while collecting their annotations. The vision is to tackle the complex problem of media bias detection with a very scalable, high quality, and gold standard data set to overcome the drawbacks of current models in the area.