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首页> 外文会议>International conference on information in contemporary society >Illegal Aliens or Undocumented Immigrants? Towards the Automated Identification of Bias by Word Choice and Labeling
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Illegal Aliens or Undocumented Immigrants? Towards the Automated Identification of Bias by Word Choice and Labeling

机译：非法外国人或无证件移民？通过单词选择和标记实现自动识别偏差

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摘要

Media bias, i.e., slanted news coverage, can strongly impact the public perception of topics reported in the news. While the analysis of media bias has recently gained attention in computer science, the automated methods and results tend to be simple when compared to approaches and results in the social sciences, where researchers have studied media bias for decades. We propose Newsalyze, a work-in-progress prototype that imitates a manual analysis concept for media bias established in the social sciences. Newsalyze aims to find instances of bias by word choice and labeling in a set of news articles reporting on the same event. Bias by word choice and labeling (WCL) occurs when journalists use different phrases to refer to the same semantic concept, e.g., actors or actions. This way, instances of bias by WCL can induce strongly divergent emotional responses from readers, such as the terms 'illegal aliens' vs. 'undocumented immigrants.' We describe two critical tasks of the analysis workflow, finding and mapping such phrases, and estimating their effects on readers. For both tasks, we also present first results, which indicate the effectiveness of exploiting methods and models from the social sciences in an automated approach.
机译：媒体偏见，即倾斜的新闻报道，可能会严重影响公众对新闻报道主题的看法。尽管最近对媒体偏见的分析已引起计算机科学的关注，但是与社会科学中研究偏见数十年的方法和结果相比，自动化的方法和结果趋于简单。我们提出了Newsalyze，这是一个进行中的原型，它模仿了社会科学中建立的媒体偏见的手动分析概念。 Newsalyze旨在通过单词选择和标签来发现偏见的实例，这些偏见的实例是在报道同一事件的一组新闻文章中。当记者使用不同的短语来指代相同的语义概念（例如演员或行为）时，就会发生按字选择和标记（WCL）的偏见。这样一来，WCL的偏见便会引起读者的强烈反感，例如“非法外星人”和“无证移民”。我们描述了分析工作流程中的两个关键任务，即查找和映射此类短语以及估计其对读者的影响。对于这两个任务，我们还展示了第一个结果，这些结果表明了以自动化方式利用社会科学方法和模型的有效性。

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