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

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 of
registers 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.