AI 中文总结
研究在线新闻语言媒体偏见检测，设计六个指标并通过两阶段实验测试其影响，发现突出偏见短语等可提高检测技能，最强预测因素是陈述与参与者政治一致性，最后给出设计建议。
AI 中文摘要
在线新闻文章中语言偏见的影响日益受到关注，尤其是在塑造公众舆论和政治两极分化加剧的背景下。虽然关于错误信息指标的文献越来越多，但尚未有足够的测试来抵消媒体偏见的影响。因此，我们设计了六个指标（偏见条、偏见量表、偏见亮点、政治量表、情感量表和信任分数），并在一个两阶段实验（n = 214）中测试它们对语言偏见检测和感知的影响。首先，我们让参与者接触简短的、类似社交媒体的陈述以及一个指标，并询问偏见感知。其次，我们通过移除指标并要求参与者标记有偏见的词语来评估偏见检测。此外，我们研究了信任、分享辨别力和情感与偏见感知和检测之间的关系。我们的结果表明，突出有偏见的短语并在量表中显示带有上下文信息的总偏见显著提高了偏见检测技能。然而，减少偏见检测的最强预测因素是陈述与参与者之间的政治一致性。我们最后给出了在线新闻环境中语言媒体偏见指标的设计建议。
英文摘要
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.
CommentsConditionally accepted for publication in IEEE Transactions on Visualization and Computer Graphics
