@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}

}

@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}

}

@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}

}

@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}

}