%0 Journal Article %1 hinterreiter2024ninja %A Hinterreiter, Smi %A Spinde, Timo %A Oberdörfer, Sebastian %A Echizen, Isao %A Latoschik, Marc Erich %D 2024 %J Proceedings of the ACM Human-Computer Interaction %K myown xrhub %N CHI PLAY, Article 327 %P 29 %R 10.1145/3677092 %T News Ninja: Gamified Annotation Of Linguistic Bias In Online News %U https://dl.acm.org/doi/10.1145/3677092 %V 8 %X 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.

@article{hinterreiter2024ninja, 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.}, added-at = {2024-07-22T12:10:23.000+0200}, author = {Hinterreiter, Smi and Spinde, Timo and Oberdörfer, Sebastian and Echizen, Isao and Latoschik, Marc Erich}, biburl = {https://www.bibsonomy.org/bibtex/2d2b68baca4e322fd58cb344b4fd074ae/hci-uwb}, doi = {10.1145/3677092}, interhash = {f702f22c84ee643b2f33ae013273dfe4}, intrahash = {d2b68baca4e322fd58cb344b4fd074ae}, journal = {Proceedings of the ACM Human-Computer Interaction}, keywords = {myown xrhub}, language = {en}, month = {October}, number = {CHI PLAY, Article 327}, pages = 29, timestamp = {2024-12-09T13:38:15.000+0100}, title = {News Ninja: Gamified Annotation Of Linguistic Bias In Online News}, url = {https://dl.acm.org/doi/10.1145/3677092}, volume = 8, year = 2024 }