Short biography
Coordination of the network, for all detailed info or CV please see https://timospinde.com/.
Contact
t.spinde <ät> media-bias-research org
References
2025
Horych, Tomas; Mandl, Christoph; Ruas, Terry; Greiner-Petter, Andre; Gipp, Bela; Aizawa, Akiko; Spinde, Timo
The Promises and Pitfalls of LLM Annotations in Dataset Labeling: a Case Study on Media Bias Detection Proceedings Article
In: Findings of the 2025 Conference of the The Nations of the Americas Chapter of the Association for Computational Linguistics: NAACL 2025, Association for Computational Linguistics, Albuquerque, USA, 2025.
Abstract | Links | BibTeX | Tags: dataset, lexical bias, LLMs, synthetic annotations
@inproceedings{Horych2025,
title = {The Promises and Pitfalls of LLM Annotations in Dataset Labeling: a Case Study on Media Bias Detection},
author = {Tomas Horych and Christoph Mandl and Terry Ruas and Andre Greiner-Petter and Bela Gipp and Akiko Aizawa and Timo Spinde},
url = {https://media-bias-research.org/wp-content/uploads/2025/01/Horych2025.pdf},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
booktitle = {Findings of the 2025 Conference of the The Nations of the Americas Chapter of the Association for Computational Linguistics: NAACL 2025},
publisher = {Association for Computational Linguistics},
address = {Albuquerque, USA},
abstract = {High annotation costs from hiring or crowd-sourcing complicate the creation of large, high-quality datasets needed for training reliable text classifiers. Recent research suggests using Large Language Models (LLMs) to automate the annotation process, reducing these costs while maintaining data quality. LLMs have shown promising results in annotating downstream tasks like hate speech detection and political framing. Building on the success in these areas, this study investigates whether LLMs are viable for annotating a complex task of media bias detection and whether a downstream media bias classifier can be trained on such data. We create Anno-lexical , the first large-scale dataset for media bias classification with over 48k synthetically annotated examples.
Our classifier fine-tuned on it surpasses all of the annotator LLMs by 5-9% in Mathew’s Cor- relation Coefficient (MCC) and performs close to or outperforms the model trained on human-labeled data when evaluated on two media bias benchmark datasets (BABE and BASIL). This study demonstrates how our approach significantly reduces the cost of dataset creation in the media bias domain and, by extension - the development of the classifiers, while our subse-quent behavioral stress-testing reveals some of its current limitations and trade-offs.},
keywords = {dataset, lexical bias, LLMs, synthetic annotations},
pubstate = {published},
tppubtype = {inproceedings}
}
High annotation costs from hiring or crowd-sourcing complicate the creation of large, high-quality datasets needed for training reliable text classifiers. Recent research suggests using Large Language Models (LLMs) to automate the annotation process, reducing these costs while maintaining data quality. LLMs have shown promising results in annotating downstream tasks like hate speech detection and political framing. Building on the success in these areas, this study investigates whether LLMs are viable for annotating a complex task of media bias detection and whether a downstream media bias classifier can be trained on such data. We create Anno-lexical , the first large-scale dataset for media bias classification with over 48k synthetically annotated examples.
Our classifier fine-tuned on it surpasses all of the annotator LLMs by 5-9% in Mathew’s Cor- relation Coefficient (MCC) and performs close to or outperforms the model trained on human-labeled data when evaluated on two media bias benchmark datasets (BABE and BASIL). This study demonstrates how our approach significantly reduces the cost of dataset creation in the media bias domain and, by extension - the development of the classifiers, while our subse-quent behavioral stress-testing reveals some of its current limitations and trade-offs.
Our classifier fine-tuned on it surpasses all of the annotator LLMs by 5-9% in Mathew’s Cor- relation Coefficient (MCC) and performs close to or outperforms the model trained on human-labeled data when evaluated on two media bias benchmark datasets (BABE and BASIL). This study demonstrates how our approach significantly reduces the cost of dataset creation in the media bias domain and, by extension - the development of the classifiers, while our subse-quent behavioral stress-testing reveals some of its current limitations and trade-offs.
Spinde, Timo
Springer Vieweg Wiesbaden, 2025, ISBN: 978-3-658-47797-4 978-3-658-47798-1.
@book{Spinde2025,
title = {Automated Detection of Media Bias: From the Conceptualization of Media Bias to its Computational Classification},
author = {Timo Spinde},
url = {https://link.springer.com/10.1007/978-3-658-47798-1},
doi = {10.1007/978-3-658-47798-1},
isbn = {978-3-658-47797-4 978-3-658-47798-1},
year = {2025},
date = {2025-01-01},
publisher = {Springer Vieweg Wiesbaden},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
Spinde, Timo; Wu, Fei; Gaissmaier, Wolfgang; Demartini, Gianluca; Echizen, Isao; Giese, Helge
Enhancing media literacy: The effectiveness of (Human) annotations and bias visualizations on bias detection Journal Article
In: Information Processing & Management, vol. 62, no. 6, pp. 104244, 2025, ISSN: 0306-4573.
Abstract | Links | BibTeX | Tags: Language processing, media bias, News literacy, Text perception
@article{SPINDE2025104244,
title = {Enhancing media literacy: The effectiveness of (Human) annotations and bias visualizations on bias detection},
author = {Timo Spinde and Fei Wu and Wolfgang Gaissmaier and Gianluca Demartini and Isao Echizen and Helge Giese},
url = {https://www.sciencedirect.com/science/article/pii/S0306457325001852},
doi = {https://doi.org/10.1016/j.ipm.2025.104244},
issn = {0306-4573},
year = {2025},
date = {2025-01-01},
journal = {Information Processing & Management},
volume = {62},
number = {6},
pages = {104244},
abstract = {Marking biased texts effectively increases media bias awareness, but its sustainability across new topics and unmarked news remains unclear, and the role of AI-generated bias labels is untested. This study examines how news consumers learn to perceive media bias from human- and AI-generated labels and identify biased language through highlighting, neutral rephrasing, and political orientation cues. We conducted two experiments with a teaching phase exposing them to various bias-labeling conditions and a testing phase evaluating their ability to classify biased sentences and detect biased text in unlabeled news on new topics. We find that, compared to the control group, both human- and AI-generated sentential bias labels significantly improve bias classification (p < .001), though human labels are more effective (d = 0.42 vs. d = 0.23). Additionally, among all teaching interventions, participants best detect biased sentences when taught with biased sentence or phrase labels (p < .001), while politicized phrase labels reduce accuracy. The effectiveness of different media literacy interventions remains independent of political ideology, but conservative participants are generally less accurate (p = .011), suggesting an interaction between political inclinations and bias detection. Our research provides a novel experimental framework into assessing the generalizability of media bias awareness and offer practical implications for designing bias indicators in news-reading platforms and media literacy curricula.},
keywords = {Language processing, media bias, News literacy, Text perception},
pubstate = {published},
tppubtype = {article}
}
Marking biased texts effectively increases media bias awareness, but its sustainability across new topics and unmarked news remains unclear, and the role of AI-generated bias labels is untested. This study examines how news consumers learn to perceive media bias from human- and AI-generated labels and identify biased language through highlighting, neutral rephrasing, and political orientation cues. We conducted two experiments with a teaching phase exposing them to various bias-labeling conditions and a testing phase evaluating their ability to classify biased sentences and detect biased text in unlabeled news on new topics. We find that, compared to the control group, both human- and AI-generated sentential bias labels significantly improve bias classification (p < .001), though human labels are more effective (d = 0.42 vs. d = 0.23). Additionally, among all teaching interventions, participants best detect biased sentences when taught with biased sentence or phrase labels (p < .001), while politicized phrase labels reduce accuracy. The effectiveness of different media literacy interventions remains independent of political ideology, but conservative participants are generally less accurate (p = .011), suggesting an interaction between political inclinations and bias detection. Our research provides a novel experimental framework into assessing the generalizability of media bias awareness and offer practical implications for designing bias indicators in news-reading platforms and media literacy curricula.
Habereder, Isabella; Kneib, Thomas; Echizen, Isao; Spinde, Timo
A Systematic Review of Spatio-Temporal Statistical Models: Theory, Structure, and Applications Miscellaneous
2025.
@misc{habereder2025systematicreviewspatiotemporalstatistical,
title = {A Systematic Review of Spatio-Temporal Statistical Models: Theory, Structure, and Applications},
author = {Isabella Habereder and Thomas Kneib and Isao Echizen and Timo Spinde},
url = {https://arxiv.org/abs/2511.00422},
doi = {https://doi.org/10.48550/arXiv.2511.00422},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
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 Forthcoming
In: Proceedings of the International AAAI Conference on Web and Social Media (ICWSM'25), AAAI, Copenhagen, Denmark, Forthcoming.
Abstract | Links | BibTeX | Tags: definition extraction, LLM, media bias, relevance classification, taxonomy building
@inproceedings{Spinde2025TaxoMatic,
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},
url = {https://media-bias-research.org/wp-content/uploads/2025/04/spinde2025.pdf},
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 = {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 framework’s 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.},
keywords = {definition extraction, LLM, media bias, relevance classification, taxonomy building},
pubstate = {forthcoming},
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 framework’s 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.
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 Forthcoming
In: Proceedings of the International AAAI Conference on Web and Social Media (ICWSM'25), AAAI, Copenhagen, Denmark, Forthcoming.
Abstract | Links | BibTeX | Tags: crowdsourcing, HITL, linguistic bias, media bias, news bias
@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},
url = {https://media-bias-research.org/wp-content/uploads/2024/07/Preprint_ICWSM_25_NewsUnfold.pdf},
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},
keywords = {crowdsourcing, HITL, linguistic bias, media bias, news bias},
pubstate = {forthcoming},
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
2024
Horych, Tomas; Wessel, Martin; Wahle, Jan Philip; Ruas, Terry; Wassmuth, Jerome; Greiner-Petter, Andre; Aizawa, Akiko; Gipp, Bela; Spinde, Timo
MAGPIE: Multi-Task Analysis of Media-Bias Generalization with Pre-Trained Identification of Expressions Proceedings Article
In: "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation", 2024.
Abstract | Links | BibTeX | Tags: dataset, multi-task learning, Transfer learning
@inproceedings{Horych2024a,
title = {MAGPIE: Multi-Task Analysis of Media-Bias Generalization with Pre-Trained Identification of Expressions},
author = {Tomas Horych and Martin Wessel and Jan Philip Wahle and Terry Ruas and Jerome Wassmuth and Andre Greiner-Petter and Akiko Aizawa and Bela Gipp and Timo Spinde},
url = {https://aclanthology.org/2024.lrec-main.952},
year = {2024},
date = {2024-02-01},
urldate = {2024-02-01},
booktitle = {"Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation"},
abstract = {Media bias detection poses a complex, multifaceted problem traditionally tackled using single-task models and small in-domain datasets, consequently lacking generalizability. To address this, we introduce MAGPIE, a large-scale multi-task pre-training approach explicitly tailored for media bias detection. To enable large-scale pre-training, we construct Large Bias Mixture (LBM), a compilation of 59 bias-related tasks. MAGPIE outperforms previous approaches in media bias detection on the Bias Annotation By Experts (BABE) dataset, with a relative improvement of 3.3% F1-score. Furthermore, using a RoBERTa encoder, we show that MAGPIE needs only 15% of fine-tuning steps compared to single-task approaches. We provide insight into task learning interference and show that sentiment analysis and emotion detection help learning of all other tasks, and scaling the number of tasks leads to the best results. MAGPIE confirms that MTL is a promising approach for addressing media bias detection, enhancing the accuracy and efficiency of existing models. Furthermore, LBM is the first available resource collection focused on media bias MTL.},
keywords = {dataset, multi-task learning, Transfer learning},
pubstate = {published},
tppubtype = {inproceedings}
}
Media bias detection poses a complex, multifaceted problem traditionally tackled using single-task models and small in-domain datasets, consequently lacking generalizability. To address this, we introduce MAGPIE, a large-scale multi-task pre-training approach explicitly tailored for media bias detection. To enable large-scale pre-training, we construct Large Bias Mixture (LBM), a compilation of 59 bias-related tasks. MAGPIE outperforms previous approaches in media bias detection on the Bias Annotation By Experts (BABE) dataset, with a relative improvement of 3.3% F1-score. Furthermore, using a RoBERTa encoder, we show that MAGPIE needs only 15% of fine-tuning steps compared to single-task approaches. We provide insight into task learning interference and show that sentiment analysis and emotion detection help learning of all other tasks, and scaling the number of tasks leads to the best results. MAGPIE confirms that MTL is a promising approach for addressing media bias detection, enhancing the accuracy and efficiency of existing models. Furthermore, LBM is the first available resource collection focused on media bias MTL.
Hinterreiter, Smi; Spinde, Timo; Oberdörfer, Sebastian; Echizen, Isao; Latoschik, Marc Erich
News Ninja: Gamified Annotation of Linguistic Bias in Online News Journal Article Forthcoming
In: Proc. ACM Hum.-Comput. Interact., vol. 8, no. CHI PLAY, Forthcoming, (Publisher: Association for Computing Machinery. Conditionally accepted for publication).
Abstract | Links | BibTeX | Tags: crowdsourcing, Game With A Purpose, linguistic bias, media bias, news bias
@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},
url = {https://media-bias-research.org/wp-content/uploads/2024/07/Preprint_News_Ninja.pdf},
doi = {10.1145/3677092},
year = {2024},
date = {2024-10-14},
urldate = {2024-10-14},
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.},
note = {Publisher: Association for Computing Machinery.
Conditionally accepted for publication},
keywords = {crowdsourcing, Game With A Purpose, linguistic bias, media bias, news bias},
pubstate = {forthcoming},
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.
2023
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 Journal Article
In: arXiv preprint, 2023.
@article{Spinde2023MediaBias,
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://media-bias-research.org/wp-content/uploads/2023/12/spinde2023.pdf},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {arXiv preprint},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Spinde, Timo; Richter, Elisabeth; Wessel, Martin; Kulshrestha, Juhi; Donnay, Karsten
What do Twitter comments tell about news article bias? Assessing the impact of news article bias on its perception on Twitter Journal Article
In: Online Social Networks and Media, vol. 37-38, pp. 100264, 2023, ISSN: 2468-6964.
Abstract | Links | BibTeX | Tags: Hate speech detection, media bias, Sentiment analysis, Transfer learning
@article{SPINDE2023100264,
title = {What do Twitter comments tell about news article bias? Assessing the impact of news article bias on its perception on Twitter},
author = {Timo Spinde and Elisabeth Richter and Martin Wessel and Juhi Kulshrestha and Karsten Donnay},
url = {https://www.sciencedirect.com/science/article/pii/S246869642300023X},
doi = {https://doi.org/10.1016/j.osnem.2023.100264},
issn = {2468-6964},
year = {2023},
date = {2023-01-01},
journal = {Online Social Networks and Media},
volume = {37-38},
pages = {100264},
abstract = {News stories circulating online, especially on social media platforms, are nowadays a primary source of information. Given the nature of social media, news no longer are just news, but they are embedded in the conversations of users interacting with them. This is particularly relevant for inaccurate information or even outright misinformation because user interaction has a crucial impact on whether information is uncritically disseminated or not. Biased coverage has been shown to affect personal decision-making. Still, it remains an open question whether users are aware of the biased reporting they encounter and how they react to it. The latter is particularly relevant given that user reactions help contextualize reporting for other users and can thus help mitigate but may also exacerbate the impact of biased media coverage. This paper approaches the question from a measurement point of view, examining whether reactions to news articles on Twitter can serve as bias indicators, i.e., whether how users comment on a given article relates to its actual level of bias. We first give an overview of research on media bias before discussing key concepts related to how individuals engage with online content, focusing on the sentiment (or valance) of comments and on outright hate speech. We then present the first dataset connecting reliable human-made media bias classifications of news articles with the reactions these articles received on Twitter. We call our dataset BAT - Bias And Twitter. BAT covers 2,800 (bias-rated) news articles from 255 English-speaking news outlets. Additionally, BAT includes 175,807 comments and retweets referring to the articles. Based on BAT, we conduct a multi-feature analysis to identify comment characteristics and analyze whether Twitter reactions correlate with an article’s bias. First, we fine-tune and apply two XLNet-based classifiers for hate speech detection and sentiment analysis. Second, we relate the results of the classifiers to the article bias annotations within a multi-level regression. The results show that Twitter reactions to an article indicate its bias, and vice-versa. With a regression coefficient of 0.703 (p<0.01), we specifically present evidence that Twitter reactions to biased articles are significantly more hateful. Our analysis shows that the news outlet’s individual stance reinforces the hate-bias relationship. In future work, we will extend the dataset and analysis, including additional concepts related to media bias.},
keywords = {Hate speech detection, media bias, Sentiment analysis, Transfer learning},
pubstate = {published},
tppubtype = {article}
}
News stories circulating online, especially on social media platforms, are nowadays a primary source of information. Given the nature of social media, news no longer are just news, but they are embedded in the conversations of users interacting with them. This is particularly relevant for inaccurate information or even outright misinformation because user interaction has a crucial impact on whether information is uncritically disseminated or not. Biased coverage has been shown to affect personal decision-making. Still, it remains an open question whether users are aware of the biased reporting they encounter and how they react to it. The latter is particularly relevant given that user reactions help contextualize reporting for other users and can thus help mitigate but may also exacerbate the impact of biased media coverage. This paper approaches the question from a measurement point of view, examining whether reactions to news articles on Twitter can serve as bias indicators, i.e., whether how users comment on a given article relates to its actual level of bias. We first give an overview of research on media bias before discussing key concepts related to how individuals engage with online content, focusing on the sentiment (or valance) of comments and on outright hate speech. We then present the first dataset connecting reliable human-made media bias classifications of news articles with the reactions these articles received on Twitter. We call our dataset BAT - Bias And Twitter. BAT covers 2,800 (bias-rated) news articles from 255 English-speaking news outlets. Additionally, BAT includes 175,807 comments and retweets referring to the articles. Based on BAT, we conduct a multi-feature analysis to identify comment characteristics and analyze whether Twitter reactions correlate with an article’s bias. First, we fine-tune and apply two XLNet-based classifiers for hate speech detection and sentiment analysis. Second, we relate the results of the classifiers to the article bias annotations within a multi-level regression. The results show that Twitter reactions to an article indicate its bias, and vice-versa. With a regression coefficient of 0.703 (p<0.01), we specifically present evidence that Twitter reactions to biased articles are significantly more hateful. Our analysis shows that the news outlet’s individual stance reinforces the hate-bias relationship. In future work, we will extend the dataset and analysis, including additional concepts related to media bias.
Wessel, Martin; Horych, Tomas; Ruas, Terry; Aizawa, Akiko; Gipp, Bela; Spinde, Timo
Introducing MBIB - the first Media Bias Identification Benchmark Task and Dataset Collection Proceedings Article
In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’23), ACM, New York, NY, USA, 2023, ISBN: 978-1-4503-9408-6/23/07.
Abstract | Links | BibTeX | Tags:
@inproceedings{Wessel2023,
title = {Introducing MBIB - the first Media Bias Identification Benchmark Task and Dataset Collection},
author = {Martin Wessel and Tomas Horych and Terry Ruas and Akiko Aizawa and Bela Gipp and Timo Spinde},
url = {https://media-bias-research.org/wp-content/uploads/2023/04/Wessel2023Preprint.pdf},
doi = {https://doi.org/10.1145/3539618.3591882},
isbn = {978-1-4503-9408-6/23/07},
year = {2023},
date = {2023-07-01},
urldate = {2023-07-01},
booktitle = {Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’23)},
publisher = {ACM},
address = {New York, NY, USA},
abstract = {Although media bias detection is a complex multi-task problem, there is, to date, no unified benchmark grouping these evaluation tasks. We introduce the Media Bias Identification Benchmark (MBIB), a comprehensive benchmark that groups different types of media bias (e.g., linguistic, cognitive, political) under a common framework to test how prospective detection techniques generalize. After reviewing 115 datasets, we select nine tasks and carefully propose 22 associated datasets for evaluating media bias detection techniques. We evaluate MBIB using state-of-the-art Transformer techniques (e.g., T5, BART). Our results suggest that while hate speech, racial bias, and gender bias are easier to detect, models struggle to handle certain bias types, e.g., cognitive and political bias. However, our results show that no single technique can outperform all the others significantly.We also find an uneven distribution of research interest and resource allocation to the individual tasks in media bias. 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.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Although media bias detection is a complex multi-task problem, there is, to date, no unified benchmark grouping these evaluation tasks. We introduce the Media Bias Identification Benchmark (MBIB), a comprehensive benchmark that groups different types of media bias (e.g., linguistic, cognitive, political) under a common framework to test how prospective detection techniques generalize. After reviewing 115 datasets, we select nine tasks and carefully propose 22 associated datasets for evaluating media bias detection techniques. We evaluate MBIB using state-of-the-art Transformer techniques (e.g., T5, BART). Our results suggest that while hate speech, racial bias, and gender bias are easier to detect, models struggle to handle certain bias types, e.g., cognitive and political bias. However, our results show that no single technique can outperform all the others significantly.We also find an uneven distribution of research interest and resource allocation to the individual tasks in media bias. 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.
2022
Spinde, Timo; Jeggle, Christin; Haupt, Magdalena; Gaissmaier, Wolfgang; Giese, Helge
How do we raise media bias awareness effectively? Effects of visualizations to communicate bias Journal Article
In: PLOS ONE, vol. 17, no. 4, pp. 1-14, 2022.
Abstract | Links | BibTeX | Tags:
@article{10.1371/journal.pone.0266204,
title = {How do we raise media bias awareness effectively? Effects of visualizations to communicate bias},
author = {Timo Spinde and Christin Jeggle and Magdalena Haupt and Wolfgang Gaissmaier and Helge Giese},
url = {https://doi.org/10.1371/journal.pone.0266204},
doi = {10.1371/journal.pone.0266204},
year = {2022},
date = {2022-01-01},
journal = {PLOS ONE},
volume = {17},
number = {4},
pages = {1-14},
publisher = {Public Library of Science},
abstract = {Media bias has a substantial impact on individual and collective perception of news. Effective communication that may counteract its potential negative effects still needs to be developed. In this article, we analyze how to facilitate the detection of media bias with visual and textual aids in the form of (a) a forewarning message, (b) text annotations, and (c) political classifiers. In an online experiment, we randomized 985 participants to receive a biased liberal or conservative news article in any combination of the three aids. Meanwhile, their subjective perception of media bias in this article, attitude change, and political ideology were assessed. Both the forewarning message and the annotations increased media bias awareness, whereas the political classification showed no effect. Incongruence between an articles’ political position and individual political orientation also increased media bias awareness. Visual aids did not mitigate this effect. Likewise, attitudes remained unaltered.},
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Media bias has a substantial impact on individual and collective perception of news. Effective communication that may counteract its potential negative effects still needs to be developed. In this article, we analyze how to facilitate the detection of media bias with visual and textual aids in the form of (a) a forewarning message, (b) text annotations, and (c) political classifiers. In an online experiment, we randomized 985 participants to receive a biased liberal or conservative news article in any combination of the three aids. Meanwhile, their subjective perception of media bias in this article, attitude change, and political ideology were assessed. Both the forewarning message and the annotations increased media bias awareness, whereas the political classification showed no effect. Incongruence between an articles’ political position and individual political orientation also increased media bias awareness. Visual aids did not mitigate this effect. Likewise, attitudes remained unaltered.
Spinde, Timo; Krieger, Jan-David; Ruas, Terry; Mitrović, Jelena; Götz-Hahn, Franz; Aizawa, Akiko; Gipp, Bela
Exploiting Transformer-based Multitask Learning for the Detection of Media Bias in News Articles Proceedings Article
In: Proceedings of the iConference 2022, Virtual event, 2022.
@inproceedings{Spinde2022a,
title = {Exploiting Transformer-based Multitask Learning for the Detection of Media Bias in News Articles},
author = {Timo Spinde and Jan-David Krieger and Terry Ruas and Jelena Mitrović and Franz Götz-Hahn and Akiko Aizawa and Bela Gipp},
url = {https://media-bias-research.org/wp-content/uploads/2022/03/Spinde2022a_mbg.pdf},
doi = {https://doi.org/10.1007/978-3-030-96957-8_20},
year = {2022},
date = {2022-03-04},
urldate = {2022-03-04},
booktitle = {Proceedings of the iConference 2022},
address = {Virtual event},
keywords = {},
pubstate = {published},
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}
Krieger, David; Spinde, Timo; Ruas, Terry; Kulshrestha, Juhi; Gipp, Bela
A Domain-adaptive Pre-training Approach for Language Bias Detection in News Proceedings Article
In: 2022 ACM/IEEE Joint Conference on Digital Libraries (JCDL), Cologne, Germany, 2022.
@inproceedings{Krieger2022,
title = {A Domain-adaptive Pre-training Approach for Language Bias Detection in News},
author = {David Krieger and Timo Spinde and Terry Ruas and Juhi Kulshrestha and Bela Gipp},
url = {https://media-bias-research.org/wp-content/uploads/2022/06/Krieger2022_mbg.pdf},
doi = {10.1145/3529372.3530932},
year = {2022},
date = {2022-06-01},
urldate = {2022-06-01},
booktitle = {2022 ACM/IEEE Joint Conference on Digital Libraries (JCDL)},
address = {Cologne, Germany},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2021
Spinde, Timo; Rudnitckaia, Lada; Mitrović, Jelena; Hamborg, Felix; Granitzer, Michael; Gipp, Bela; Donnay, Karsten
Automated identification of bias inducing words in news articles using linguistic and context-oriented features Journal Article
In: Information Processing & Management, vol. 58, no. 3, pp. 102505, 2021, ISSN: 0306-4573.
Abstract | Links | BibTeX | Tags: bias data set, context analysis, feature engineering, media bias, news analysis, text analysis
@article{SPINDE2021102505,
title = {Automated identification of bias inducing words in news articles using linguistic and context-oriented features},
author = {Timo Spinde and Lada Rudnitckaia and Jelena Mitrović and Felix Hamborg and Michael Granitzer and Bela Gipp and Karsten Donnay},
url = {https://www.sciencedirect.com/science/article/pii/S0306457321000157/pdfft?md5=64e81212b3bfa861d01a6fe3d5b979c3&pid=1-s2.0-S0306457321000157-main.pdf},
doi = {https://doi.org/10.1016/j.ipm.2021.102505},
issn = {0306-4573},
year = {2021},
date = {2021-01-01},
journal = {Information Processing & Management},
volume = {58},
number = {3},
pages = {102505},
abstract = {Media has a substantial impact on public perception of events, and, accordingly, the way media presents events can potentially alter the beliefs and views of the public. One of the ways in which bias in news articles can be introduced is by altering word choice. Such a form of bias is very challenging to identify automatically due to the high context-dependence and the lack of a large-scale gold-standard data set. In this paper, we present a prototypical yet robust and diverse data set for media bias research. It consists of 1,700 statements representing various media bias instances and contains labels for media bias identification on the word and sentence level. In contrast to existing research, our data incorporate background information on the participants’ demographics, political ideology, and their opinion about media in general. Based on our data, we also present a way to detect bias-inducing words in news articles automatically. Our approach is feature-oriented, which provides a strong descriptive and explanatory power compared to deep learning techniques. We identify and engineer various linguistic, lexical, and syntactic features that can potentially be media bias indicators. Our resource collection is the most complete within the media bias research area to the best of our knowledge. We evaluate all of our features in various combinations and retrieve their possible importance both for future research and for the task in general. We also evaluate various possible Machine Learning approaches with all of our features. XGBoost, a decision tree implementation, yields the best results. Our approach achieves an F1-score of 0.43, a precision of 0.29, a recall of 0.77, and a ROC AUC of 0.79, which outperforms current media bias detection methods based on features. We propose future improvements, discuss the perspectives of the feature-based approach and a combination of neural networks and deep learning with our current system.},
keywords = {bias data set, context analysis, feature engineering, media bias, news analysis, text analysis},
pubstate = {published},
tppubtype = {article}
}
Media has a substantial impact on public perception of events, and, accordingly, the way media presents events can potentially alter the beliefs and views of the public. One of the ways in which bias in news articles can be introduced is by altering word choice. Such a form of bias is very challenging to identify automatically due to the high context-dependence and the lack of a large-scale gold-standard data set. In this paper, we present a prototypical yet robust and diverse data set for media bias research. It consists of 1,700 statements representing various media bias instances and contains labels for media bias identification on the word and sentence level. In contrast to existing research, our data incorporate background information on the participants’ demographics, political ideology, and their opinion about media in general. Based on our data, we also present a way to detect bias-inducing words in news articles automatically. Our approach is feature-oriented, which provides a strong descriptive and explanatory power compared to deep learning techniques. We identify and engineer various linguistic, lexical, and syntactic features that can potentially be media bias indicators. Our resource collection is the most complete within the media bias research area to the best of our knowledge. We evaluate all of our features in various combinations and retrieve their possible importance both for future research and for the task in general. We also evaluate various possible Machine Learning approaches with all of our features. XGBoost, a decision tree implementation, yields the best results. Our approach achieves an F1-score of 0.43, a precision of 0.29, a recall of 0.77, and a ROC AUC of 0.79, which outperforms current media bias detection methods based on features. We propose future improvements, discuss the perspectives of the feature-based approach and a combination of neural networks and deep learning with our current system.
Ehrhardt, Jonas; Spinde, Timo; Vardasbi, Ali; Hamborg, Felix
Omission of Information: Identifying Political Slant via an Analysis of Co-occurring Entities Book Section
In: Information between Data and Knowledge, vol. 74, pp. 80–93, Werner Hülsbusch, Glückstadt, 2021, (Session 2: Information Behavior and Information Literacy 2).
Abstract | Links | BibTeX | Tags: media bias; bias by omission; news articles; co-occurrences
@incollection{epub44939,
title = {Omission of Information: Identifying Political Slant via an Analysis of Co-occurring Entities},
author = {Jonas Ehrhardt and Timo Spinde and Ali Vardasbi and Felix Hamborg},
url = {https://epub.uni-regensburg.de/44939/},
year = {2021},
date = {2021-01-01},
booktitle = {Information between Data and Knowledge},
volume = {74},
pages = {80–93},
publisher = {Werner Hülsbusch},
address = {Glückstadt},
series = {Schriften zur Informationswissenschaft},
abstract = {Due to the strong impact the news has on society, the detection and analysis of bias within the media are important topics. Most approaches to bias detection focus on linguistic forms of bias or the evaluation and tracing of sources. In this paper, we present an approach that analyzes co-occurrences of entities across articles of different news outlets to indicate a strong but difficult to detect form of bias: bias by omission of information. Specifically, we present and evaluate different methods of identifying entity co-occurrences and then use the best performing method, reference entity detection, to analyze the coverage of nine major US news outlets over one year. We set a low performing but transparent baseline, which is able to identify a news outlet?s affiliation towards a political orientation. Our approach employing reference entity selection, i. e., analyzing how often one entity co-occurs with others across a set of documents, yields an F1-score of F1 = 0.51 compared to F1 = 0.20 of the TF-IDF baseline.},
note = {Session 2: Information Behavior and Information Literacy 2},
keywords = {media bias; bias by omission; news articles; co-occurrences},
pubstate = {published},
tppubtype = {incollection}
}
Due to the strong impact the news has on society, the detection and analysis of bias within the media are important topics. Most approaches to bias detection focus on linguistic forms of bias or the evaluation and tracing of sources. In this paper, we present an approach that analyzes co-occurrences of entities across articles of different news outlets to indicate a strong but difficult to detect form of bias: bias by omission of information. Specifically, we present and evaluate different methods of identifying entity co-occurrences and then use the best performing method, reference entity detection, to analyze the coverage of nine major US news outlets over one year. We set a low performing but transparent baseline, which is able to identify a news outlet?s affiliation towards a political orientation. Our approach employing reference entity selection, i. e., analyzing how often one entity co-occurs with others across a set of documents, yields an F1-score of F1 = 0.51 compared to F1 = 0.20 of the TF-IDF baseline.
Spinde, Timo; Rudnitckaia, Lada; Hamborg, Felix; Bela,; Gipp,
Identification of Biased Terms in News Articles by Comparison of Outlet-specific Word Embeddings Proceedings Article
In: Proceedings of the iConference 2021, Beijing, China (Virtual Event), 2021.
@inproceedings{Spinde2021Embeddings,
title = {Identification of Biased Terms in News Articles by Comparison of Outlet-specific Word Embeddings},
author = {Timo Spinde and Lada Rudnitckaia and Felix Hamborg and Bela and Gipp},
url = {https://media-bias-research.org/wp-content/uploads/2021/01/Spinde2021.pdf},
doi = {10.1007/978-3-030-71305-8_17},
year = {2021},
date = {2021-03-01},
urldate = {2021-03-01},
booktitle = {Proceedings of the iConference 2021},
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Spinde, Timo; Rudnitckaia, Lada; Kanishka, Sinha; Hamborg, Felix; Bela,; Gipp,; Donnay, Karsten
MBIC – A Media Bias Annotation Dataset Including Annotator Characteristics Proceedings Article
In: Proceedings of the iConference 2021, Beijing, China (Virtual Event), 2021.
@inproceedings{Spinde2021MBIC,
title = {MBIC – A Media Bias Annotation Dataset Including Annotator Characteristics},
author = {Timo Spinde and Lada Rudnitckaia and Sinha Kanishka and Felix Hamborg and Bela and Gipp and Karsten Donnay},
url = {https://media-bias-research.org/wp-content/uploads/2021/01/Spinde2021a.pdf},
doi = {10.6084/m9.figshare.17192924},
year = {2021},
date = {2021-03-01},
urldate = {2021-03-01},
booktitle = {Proceedings of the iConference 2021},
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}
Spinde, Timo; Kreuter, Christina; Gaissmaier, Wolfgang; Hamborg, Felix; Gipp, Bela; Giese, Helge
Do You Think It’s Biased? How To Ask For The Perception Of Media Bias Proceedings Article
In: Proceedings of the ACM/IEEE Joint Conference on Digital Libraries (JCDL), 2021.
@inproceedings{Spinde2021e,
title = {Do You Think It’s Biased? How To Ask For The Perception Of Media Bias},
author = {Timo Spinde and Christina Kreuter and Wolfgang Gaissmaier and Felix Hamborg and Bela Gipp and Helge Giese},
url = {https://media-bias-research.org/wp-content/uploads/2022/01/Spinde2021e.pdf},
doi = {10.1109/JCDL52503.2021.00018},
year = {2021},
date = {2021-09-01},
urldate = {2021-09-01},
booktitle = {Proceedings of the ACM/IEEE Joint Conference on Digital Libraries (JCDL)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
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Spinde, Timo; Sinha, Kanishka; Meuschke, Norman; Gipp, Bela
TASSY - A Text Annotation Survey System Proceedings Article
In: Proceedings of the ACM/IEEE Joint Conference on Digital Libraries (JCDL), 2021.
@inproceedings{Spinde2021c,
title = {TASSY - A Text Annotation Survey System},
author = {Timo Spinde and Kanishka Sinha and Norman Meuschke and Bela Gipp},
url = {https://media-bias-research.org/wp-content/uploads/2022/01/Spinde2021c.pdf},
doi = {10.1109/JCDL52503.2021.00052},
year = {2021},
date = {2021-09-01},
urldate = {2021-09-01},
booktitle = {Proceedings of the ACM/IEEE Joint Conference on Digital Libraries (JCDL)},
keywords = {},
pubstate = {published},
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}
Spinde, Timo; Krieger, David; Plank, Manu; Gipp, Bela
Towards A Reliable Ground-Truth For Biased Language Detection Proceedings Article
In: Proceedings of the ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL), Virtual Event, 2021.
@inproceedings{Spinde2021d,
title = {Towards A Reliable Ground-Truth For Biased Language Detection},
author = {Timo Spinde and David Krieger and Manu Plank and Bela Gipp},
url = {https://media-bias-research.org/wp-content/uploads/2022/01/Spinde2021d.pdf},
doi = {10.1109/JCDL52503.2021.00053},
year = {2021},
date = {2021-09-01},
urldate = {2021-09-01},
booktitle = {Proceedings of the ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL)},
address = {Virtual Event},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Spinde, Timo
An Interdisciplinary Approach for the Automated Detection and Visualization of Media Bias in News Articles Proceedings Article
In: 2021 IEEE International Conference on Data Mining Workshops (ICDMW), 2021.
Links | BibTeX | Tags: media bias, news analysis, slanted coverage, text retrieval
@inproceedings{spinde2021g,
title = {An Interdisciplinary Approach for the Automated Detection and Visualization of Media Bias in News Articles},
author = {Timo Spinde},
url = {https://media-bias-research.org/wp-content/uploads/2021/09/Spinde2021g.pdf},
doi = {10.1109/ICDMW53433.2021.00144},
year = {2021},
date = {2021-09-30},
urldate = {2021-09-30},
booktitle = {2021 IEEE International Conference on Data Mining Workshops (ICDMW)},
keywords = {media bias, news analysis, slanted coverage, text retrieval},
pubstate = {published},
tppubtype = {inproceedings}
}
Spinde, Timo; Plank, Manuel; Krieger, Jan-David; Ruas, Terry; Gipp, Bela; Aizawa, Akiko
Neural Media Bias Detection Using Distant Supervision With BABE - Bias Annotations By Experts Proceedings Article
In: Findings of the Association for Computational Linguistics: EMNLP 2021, Dominican Republic, 2021.
@inproceedings{Spinde2021f,
title = {Neural Media Bias Detection Using Distant Supervision With BABE - Bias Annotations By Experts},
author = {Timo Spinde and Manuel Plank and Jan-David Krieger and Terry Ruas and Bela Gipp and Akiko Aizawa},
url = {https://media-bias-research.org/wp-content/uploads/2022/01/Neural_Media_Bias_Detection_Using_Distant_Supervision_With_BABE___Bias_Annotations_By_Experts_MBG.pdf},
doi = {10.18653/v1/2021.findings-emnlp.101},
year = {2021},
date = {2021-11-01},
urldate = {2021-11-01},
booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2021},
address = {Dominican Republic},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2020
Spinde, Timo; Hamborg, Felix; Gipp, Bela
An Integrated Approach to Detect Media Bias in German News Articles Proceedings Article
In: Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020, pp. 505–506, Association for Computing Machinery, Virtual Event, China, 2020, ISBN: 9781450375856.
Abstract | Links | BibTeX | Tags: content analysis, frame analysis, media bias, news bias, news slant
@inproceedings{10.1145/3383583.3398585,
title = {An Integrated Approach to Detect Media Bias in German News Articles},
author = {Timo Spinde and Felix Hamborg and Bela Gipp},
url = {https://doi.org/10.1145/3383583.3398585},
doi = {10.1145/3383583.3398585},
isbn = {9781450375856},
year = {2020},
date = {2020-01-01},
booktitle = {Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020},
pages = {505–506},
publisher = {Association for Computing Machinery},
address = {Virtual Event, China},
series = {JCDL '20},
abstract = {Media bias may often affect individuals' opinions on reported topics. Many existing methods that aim to identify such bias forms employ individual, specialized techniques and focus only on English texts. We propose to combine the state-of-the-art in order to further improve the performance in bias identification. Our prototype consists of three analysis components to identify media bias words in German news articles. We use an IDF-based component, a component utilizing a topic-dependent bias dictionary created using word embeddings, and an extensive dictionary of German emotional terms compiled from multiple sources. Finally, we discuss two not yet implemented analysis components that use machine learning and network analysis to identify media bias. All dictionary-based analysis components are experimentally extended with the use of general word embeddings. We also show the results of a user study.},
keywords = {content analysis, frame analysis, media bias, news bias, news slant},
pubstate = {published},
tppubtype = {inproceedings}
}
Media bias may often affect individuals' opinions on reported topics. Many existing methods that aim to identify such bias forms employ individual, specialized techniques and focus only on English texts. We propose to combine the state-of-the-art in order to further improve the performance in bias identification. Our prototype consists of three analysis components to identify media bias words in German news articles. We use an IDF-based component, a component utilizing a topic-dependent bias dictionary created using word embeddings, and an extensive dictionary of German emotional terms compiled from multiple sources. Finally, we discuss two not yet implemented analysis components that use machine learning and network analysis to identify media bias. All dictionary-based analysis components are experimentally extended with the use of general word embeddings. We also show the results of a user study.
Spinde, Timo; Hamborg, Felix; Donnay, Karsten; Becerra, Angelica; Gipp, Bela
Enabling News Consumers to View and Understand Biased News Coverage: A Study on the Perception and Visualization of Media Bias Proceedings Article
In: Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020, pp. 389–392, Association for Computing Machinery, Virtual Event, China, 2020, ISBN: 9781450375856.
Abstract | Links | BibTeX | Tags: bias visualization, news bias, news slant, perception of news
@inproceedings{10.1145/3383583.3398619,
title = {Enabling News Consumers to View and Understand Biased News Coverage: A Study on the Perception and Visualization of Media Bias},
author = {Timo Spinde and Felix Hamborg and Karsten Donnay and Angelica Becerra and Bela Gipp},
url = {https://doi.org/10.1145/3383583.3398619},
doi = {10.1145/3383583.3398619},
isbn = {9781450375856},
year = {2020},
date = {2020-01-01},
booktitle = {Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020},
pages = {389–392},
publisher = {Association for Computing Machinery},
address = {Virtual Event, China},
series = {JCDL '20},
abstract = {Traditional media outlets are known to report political news in a biased way, potentially affecting the political beliefs of the audience and even altering their voting behaviors. Many researchers focus on automatically detecting and identifying media bias in the news, but only very few studies exist that systematically analyze how theses biases can be best visualized and communicated. We create three manually annotated datasets and test varying visualization strategies. The results show no strong effects of becoming aware of the bias of the treatment groups compared to the control group, although a visualization of hand-annotated bias communicated bias in-stances more effectively than a framing visualization. Showing participants an overview page, which opposes different viewpoints on the same topic, does not yield differences in respondents' bias perception. Using a multilevel model, we find that perceived journalist bias is significantly related to perceived political extremeness and impartiality of the article.},
keywords = {bias visualization, news bias, news slant, perception of news},
pubstate = {published},
tppubtype = {inproceedings}
}
Traditional media outlets are known to report political news in a biased way, potentially affecting the political beliefs of the audience and even altering their voting behaviors. Many researchers focus on automatically detecting and identifying media bias in the news, but only very few studies exist that systematically analyze how theses biases can be best visualized and communicated. We create three manually annotated datasets and test varying visualization strategies. The results show no strong effects of becoming aware of the bias of the treatment groups compared to the control group, although a visualization of hand-annotated bias communicated bias in-stances more effectively than a framing visualization. Showing participants an overview page, which opposes different viewpoints on the same topic, does not yield differences in respondents' bias perception. Using a multilevel model, we find that perceived journalist bias is significantly related to perceived political extremeness and impartiality of the article.
Spinde, Timo; Hamborg, Felix; Gipp, Bela
Media Bias in German News Articles : A Combined Approach Proceedings Article
In: Proceedings of the 8th International Workshop on News Recommendation and Analytics ( INRA 2020), Virtual event, 2020.
@inproceedings{Spinde2020,
title = {Media Bias in German News Articles : A Combined Approach},
author = {Timo Spinde and Felix Hamborg and Bela Gipp},
url = {https://media-bias-research.org/wp-content/uploads/2021/01/Media-Bias-in-German-News-Articles-A-Combined-Approach.pdf},
doi = {10.1007/978-3-030-65965-3_41},
year = {2020},
date = {2020-09-01},
urldate = {2020-09-01},
booktitle = {Proceedings of the 8th International Workshop
on News Recommendation and Analytics ( INRA 2020)},
address = {Virtual event},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}