Visual Indicators to Increase the Detection of Linguistic Media Bias
Authors
Smi Hinterreiter (University of Würzburg), Anna Chelsea Bahß (University of Konstanz), Ann-Christin Gah (Technical University of Munich), Timo Spinde (University of Göttingen), Isao Echizen (National Institute of Informatics), Marc Erich Latoschik (University of Würzburg)
Presentation
- Session
- I'm not so certain
- Time
- Thursday, Nov 12, 08:36 – 08:48 (US/Eastern) · session 08:00 – 09:30
- Location
- Hall Essex north
Links
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Keywords
Media bias, news bias, media literacy, news literacy, information literacy, online news, partisan bias.
Abstract
The influence of linguistic bias in online news articles is a growing concern, particularly in the context of shaping public opinion and rising political polarization. While there is a growing body of literature on indicators for misinformation, none have been sufficiently tested to counteract the influence of media bias. Hence, we design six indicators (Bias Bar, Bias Gauge, Bias Highlights, Political Scale, Sentiment Scale, and Trust Score) and test their impact on linguistic bias detection and perception in a two-phased experiment (n = 214). First, we expose participants to short, social-media-like statements along with one indicator and query bias perception. Second, we evaluate bias detection by removing the indicator and asking participants to mark biased words. In addition, we examine how trust, sharing discernment, and sentiment relate to bias perception and detection. Our results show that highlighting biased phrases and showing total bias with contextual information in a gauge significantly improve bias detection skills. However, the strongest predictor for reduced bias detection was political congruency between the statement and the participant. We conclude with design recommendations for linguistic media bias indicators in online news environments.
For Practitioners
This paper would interest HCI researchers, media literacy and misinformation educators, news platform designers, and practitioners who build credibility or bias cues for online news tools. It is also useful for data journalists, civic tech teams, and NLP product teams working on bias-detection or annotation interfaces. Practitioners can use the results to favor in-text highlights and interpretable indicators such as bias gauges over abstract summary scores when the goal is to help users spot biased wording or similar content. Visualization researchers could use this paper as a starting point for designing indicators that help readers improve bias detection and critical reading.