Seven Initial Prominent Sources of All Information Bias Impartiality Types Parsed

Pub Date : 2023-06-26 DOI:10.34135/mlar-23-01-03
Erik Bean
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Abstract

Ever since information was first operationalized by library science into consumer formats, media bias has been studied from the purview of information gatekeepers who decide what, how, and when to publish based on story importance and factors like circulation. This concept did not include individuals or entities outside of the journalism discipline. With the advent of the internet and a number of social media networks that soon followed, individuals could more effectively release information without waiting for gatekeepers, thus shaping the public’s perception regardless of the topic. Scholars offered a theoretical framework for shaping the public’s opinion and still other scholars focused on how information could be slanted or partisan. However, these seminal approaches did not operationalize the term information bias in terms of the overall partiality of major sources themselves. Information evaluation tests such as the Currency, Relevance, Authority, Accuracy, and Purpose (CRAAP) and Stop, Investigate, Find, Trace (SIFT) that have been discussed as tools to assess information for bias fall short on the very first step of what to inspect and how to sort. With a gap in the literature sorting through the types of biases can be daunting and confusing. The purpose of this paper is to propose one initial method as the first step to sort information bias regardless of its form, analog or digital, into seven prominent sources each with their own inherent but larger impartiality tied to it. The sources of all information bias to be discussed in alphabetical order are: 1) academic, 2) forprofit, 3) government, 4) hidden agenda, 5) individuals, 6) nonprofit, and 7) watchdog groups.
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解析的所有信息偏差公正性类型的七个最初突出来源
自从图书馆科学首次将信息转化为消费者格式以来,媒体偏见就一直从信息看门人的角度进行研究,他们根据故事的重要性和发行量等因素来决定发布什么、如何发布以及何时发布。这一概念不包括新闻学科之外的个人或实体。随着互联网和随后出现的许多社交媒体网络的出现,个人可以更有效地发布信息,而无需等待看门人,从而塑造公众的看法,无论话题如何。学者们为塑造公众舆论提供了一个理论框架,而其他学者则关注信息如何倾斜或带有党派色彩。然而,就主要来源本身的总体偏好而言,这些开创性的方法并没有将信息偏见一词付诸实践。信息评估测试,如货币、相关性、权威性、准确性和目的(CRAAP)和停止、调查、查找、追踪(SIFT),作为评估信息偏见的工具,在检查什么和如何分类的第一步上做得不够。由于文献中存在空白,对偏见类型的分类可能会令人生畏和困惑。本文的目的是提出一种初始方法,作为第一步,将信息偏见(无论其形式是模拟还是数字)分为七个突出的来源,每个来源都有其固有的但更大的公正性。所有信息偏见的来源按字母顺序讨论:1)学术性,2)营利性,3)政府,4)隐藏议程,5)个人,6)非营利组织和7)监督组织。
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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