Facts, Fabrication, & False Information: linguistic analysis of fake news

2016年，美国爱达荷州双子瀑布镇因涉及四名7至14岁的男孩和一名5岁女孩的性行为不端指控而引起全国关注。这起事件-后来被命名为Fawnbrook案件-很快就演变成了一个充斥着错误信息的媒体马戏团。以传播阴谋论信仰和宣传极端保守议程而闻名的利基媒体开始声称，这四个男孩是叙利亚难民，他们用枪指着五岁的女孩轮奸，这与最初的故事截然不同(贝尔，2017)。从那时起，Fawnbrook案件一直被视为虚假信息传播速度有多快的标志性例子。我提议的博士研究将研究语言是如何被用来传播错误和虚假信息的。为了恰当地解决这个问题，我将结合使用语料库分析和评估分析。语料库分析提供了一种定量的方法来研究数据，而评估是一种定性的方法，考察了语篇中的人际立场。语料库分析和评价分析相结合的好处是我能够分析不同语域和体裁的语篇(Martin&White，2005)。语言学研究人员已经证明，评估与UAM语料库工具(O‘Donnell，2016)等语料库检索程序相结合，是衡量评估意义和变量统计重要性的有用方法组合(Gales，2011；Hurt，2019；Biber，2014)。评价分析也被成功地用于研究跨文化交际中的差异如何影响法律结果(Martin和Zappavigna，2016)。同样，评估本可以用来研究作者在写Fawnbrook案件时的认知立场(承诺的标志)和情感立场(情感标志)。在我的项目中，我将使用评估来探索作者如何使用立场标记来传播虚假信息，而不是可信的、事实的信息。通过这种方式，我提出的研究不仅将为现有(和相关的)问题提供一个新的视角，而且还将使用在方法上合理的技术来做到这一点。目前，虚假信息的传播是通过使用自动的算法事实核查程序来打击的；机器被用来识别(可能的)虚假信息的实例。一旦被识别，人类就会评估被标记的实例。这是由于一个明显的计算限制：计算机还不能解释自然语言的语义和语用方面(Liddy，2001；Akbik等人，2018)。通过研究语言是如何被用来传播虚假信息的，我将加强现有的关于错误信息和欺骗标记的语言信号的文献(酱&Wilson，2018)。此外，我的研究结果可以为自然语言处理(NLP)检测做出贡献，并为整个社会识别虚假信息提供可靠的数据。

In 2016, the town of Twin Falls, Idaho, USA gained national attention due to allegations of sexual misconduct involving four boys ages seven to fourteen and a five year old girl. The incident -later named the Fawnbrook Case- quickly devolved into a media circus rife with misinformation. Niche media outlets known for spreading conspiratorial beliefs and promoting ultra-conservative agendas began claiming that the four boys in question were Syrian refugees who had gang raped the five year old girl at gunpoint, a polarized departure from the original story (Bell, 2017). Since then, the Fawnbrook Case has been viewed as a hallmark example of how quickly false information can spread.My proposed doctoral research will examine how language is used to promulgate mis- and disinformation. To properly address this topic, I will use a combination of corpus analysis and Appraisal analysis. Corpus analysis offers a quantitative approach to study data while Appraisal is a qualitative approach that examines interpersonal stance across texts. The benefit of combining both corpus analysis and Appraisal analysis is that I am able to analyze texts from a variety ofregisters and genres (Martin & White, 2005). Linguistic researchers have demonstrated that Appraisal -in combination with corpus concordance programs such as the UAM CorpusTool (O'Donnell, 2016)- is a useful combination of methodologies to measure statistical significance of Appraisal meanings and variables (Gales, 2011; Hurt, 2019; Biber, 2014). Appraisal analysis has also been used to successfully study how differences in cross-cultural communication have influenced legal outcomes (Martin and Zappavigna, 2016). Similarly, Appraisal could have been used to study both epistemic stance (markers of commitment) and affective stance (markers of emotion) of authors writing about the Fawnbrook Case. For the purposes of my project, I will use Appraisal to explore how authors use stance markers to circulate false information compared to credible, factual information. In this way, my proposed research will not only offer a new perspective on an existing (and relevant) issue, but use methodologically sound techniques to do so. At present, the spread of false information is combated through the use of automated, algorithmic fact checkers; machines are used to identify instances of (possible) false information. Once identified, humans evaluate the flagged instance. This is due to a glaring computational limitation: computers cannot yet interpret semantic and pragmatic aspects of natural language (Liddy, 2001; Akbik et al., 2018). By studying how language is used to circulate false information, I will strengthen existing literature on the linguistic signaling of misinformation and deception markers (Jiang & Wilson, 2018). Moreover, the results of my research can contribute to Natural Language Processing (NLP) detection and provide sound data for society at large to identify false information.