Omission of Information: Identifying Political Slant via an Analysis of Co-occurring Entities

J. Ehrhardt, Timo Spinde, Ali Vardasbi, Felix Hamborg
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引用次数: 5

Abstract

Due to the strong impact the news has on society, the detection and analysis of bias within the media are important topics. Most approaches to bias detection focus on linguistic forms of bias or the evaluation and tracing of sources. In this paper, we present an approach that analyzes co - occurrences of entities across articles of different news outlets to indicate a strong but difficult to detect form of bias: bias by omission of information. Specifically, we present and evaluate different methods of identifying entity co - occurrences and then use the best performing method, reference entity detection, to analyze the coverage of nine major US news outlets over one year. We set a low performing but transparent baseline, which is able to identify a news outlet’s affiliation towards a political orientation. Our approach employing reference entity selection, i. e., analyzing how often one entity co - occurs with others across a set of documents, yields an F1 score of F1 = 0.51 compared to F1 = 0.20 of the TF - IDF baseline. for further testing. Those approaches did not show high performance, they did not the peculiarities of bias by omission of information.
信息遗漏:通过对共存实体的分析来识别政治倾向
由于新闻对社会的强烈影响,发现和分析媒体中的偏见是一个重要的课题。大多数偏见检测方法都集中在偏见的语言形式或来源的评估和追踪上。在本文中,我们提出了一种分析不同新闻媒体文章中实体共现的方法,以表明一种强烈但难以检测的偏见形式:信息遗漏的偏见。具体来说,我们提出并评估了识别实体共现的不同方法,然后使用表现最好的方法,参考实体检测,来分析美国九家主要新闻媒体在一年内的报道。我们设置了一个低绩效但透明的基准,它能够识别新闻媒体对政治取向的隶属关系。我们的方法采用参考实体选择,即分析一个实体在一组文档中与其他实体共同出现的频率,与TF - IDF基线的F1 = 0.20相比,F1得分为F1 = 0.51。以作进一步测试。这些方法没有显示出高的性能,它们没有由于信息遗漏而产生偏见的特性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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