Publications
2025
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 Geplante Veröffentlichung
In: Proceedings of the International AAAI Conference on Web and Social Media (ICWSM'25), AAAI, Copenhagen, Denmark, Geplante Veröffentlichung, (Conditionally accepted for publication).
Abstract | Links | BibTeX | Schlagwörter: 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},
note = {Conditionally accepted for publication},
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
Hinterreiter, Smi; Spinde, Timo; Oberdörfer, Sebastian; Echizen, Isao; Latoschik, Marc Erich
News Ninja: Gamified Annotation of Linguistic Bias in Online News Artikel Geplante Veröffentlichung
In: Proc. ACM Hum.-Comput. Interact., Bd. 8, Nr. CHI PLAY, Geplante Veröffentlichung, (Publisher: Association for Computing Machinery. Conditionally accepted for publication).
Abstract | Links | BibTeX | Schlagwörter: 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.
Zustimmung verwalten
Um dir ein optimales Erlebnis zu bieten, verwenden wir Technologien wie Cookies, um Geräteinformationen zu speichern und/oder darauf zuzugreifen. Wenn du diesen Technologien zustimmst, können wir Daten wie das Surfverhalten oder eindeutige IDs auf dieser Website verarbeiten. Wenn du deine Zustimmung nicht erteilst oder zurückziehst, können bestimmte Merkmale und Funktionen beeinträchtigt werden.
Funktional Immer aktiv
Die technische Speicherung oder der Zugang ist unbedingt erforderlich für den rechtmäßigen Zweck, die Nutzung eines bestimmten Dienstes zu ermöglichen, der vom Teilnehmer oder Nutzer ausdrücklich gewünscht wird, oder für den alleinigen Zweck, die Übertragung einer Nachricht über ein elektronisches Kommunikationsnetz durchzuführen.
Vorlieben
Die technische Speicherung oder der Zugriff ist für den rechtmäßigen Zweck der Speicherung von Präferenzen erforderlich, die nicht vom Abonnenten oder Benutzer angefordert wurden.
Statistiken
Die technische Speicherung oder der Zugriff, der ausschließlich zu statistischen Zwecken erfolgt.
Die technische Speicherung oder der Zugriff, der ausschließlich zu anonymen statistischen Zwecken verwendet wird. Ohne eine Vorladung, die freiwillige Zustimmung deines Internetdienstanbieters oder zusätzliche Aufzeichnungen von Dritten können die zu diesem Zweck gespeicherten oder abgerufenen Informationen allein in der Regel nicht dazu verwendet werden, dich zu identifizieren.
Marketing
Die technische Speicherung oder der Zugriff ist erforderlich, um Nutzerprofile zu erstellen, um Werbung zu versenden oder um den Nutzer auf einer Website oder über mehrere Websites hinweg zu ähnlichen Marketingzwecken zu verfolgen.