@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}

}