Automatic large-scale political bias detection of news outlets
Political bias is an inescapable characteristic in news and media reporting, and understanding what political biases people are exposed to when interacting with online news is of crucial import. However, quantifying political bias is problematic. To systematically study the political biases of online news, much of previous research has used human-labelled databases. Yet, these databases tend to be costly, and cover only a few thousand instances at most. Additionally, despite the wide recognition that bias can be expressed in a multitude of ways, many have only examined narrow expressions of bias. For example, most have focused on biased wording in news articles, but ignore bias expressed when an outlet avoids reporting on certain topics or events. In this article, we introduce a data-driven approach that uses machine learning techniques to analyse multiple forms of bias, and that can estimate the political leaning of hundreds of thousands of Web domains with high accuracy. Crucially, this approach also allows us to provide detailed explanations for why a news outlet is assigned a particular political bias. Our work thereby presents a scalable and comprehensive approach to studying political bias in news on a larger scale than ever before.
Interested? Read it here!
Citation: Rönnback R, Emmery C, Brighton H (2025) Automatic large-scale political bias detection of news outlets. PLoS One 20(5): e0321418. https://doi.org/10.1371/journal.pone.0321418
Ethical Concerns About Personhood, Responsibility, and Privacy in Active and Passive Brain-Computer Interfaces
Brain Computer Interfaces (BCIs) are intelligent systems that enable direct communication between the human brain and machines. While BCI systems are promising for future medical and non-medical applications, studies concerning their ethical considerations are growing. However, no previous study has examined how the public’s ethical perception of the BCI technology is affected by the particular BCI type in question. This study thus considered whether the public experienced active and passive BCIs differently in the prominent ethical domains of personhood, responsibility and privacy. Results suggest that active BCIs induce a higher ethical concern regarding personhood, and that women experienced privacy to be more concerning in passive BCIs. There were no other significant differences between the two BCI types in the examined ethical domains. A regression analysis also indicated that a person’s general ethical concern for BCIs was unaffected by their demographical information. This study provides preliminary insights for the development of ethically informed BCI systems.
Citation: Rönnback, R., Blom, F., Alimardani, M. (2024). Ethical Concerns About Personhood, Responsibility, and Privacy in Active and Passive Brain-Computer Interfaces. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2023. Lecture Notes in Networks and Systems, vol 822. Springer, Cham. https://doi.org/10.1007/978-3-031-47721-8_12
Interested? Read it here!