Common frames for conceptualizing mental illness in news media and their effect on public attitudes

对精神疾病的污名化态度构成了一个重大的社会问题，其不利影响渗透到受影响者生活的各个领域。除其他外，污名化导致获得护理的障碍和继续接受治疗的可能性降低（科里根等人，2014年），难以获得和保持就业（科里根和沃森，2002年）以及社区环境中的社会距离增加（亨德森等人，2016年）。根据“污名呐喊”调查，十分之九的服务使用者认为污名化对其生活产生了负面影响（“改变的时刻”，2008年）。迄今为止，英国最大的反污名化运动是“改变的时候”运动。到目前为止，“变革时刻”分三个阶段（2007-2011年; 2011-2015年; 2016-2021年）运作，重点是消除精神疾病的污名化。虽然这一运动有助于改变对精神疾病的态度，但为了成功和持久地减少耻辱感，需要进一步研究精神疾病是如何在公共话语中被构建的，以及精神疾病的某些话语框架是如何促成不同态度的。在这个项目中，我将研究在“改变的时刻”之前和期间，大众媒体中用于构建精神疾病的语言运动，以及这种语言如何实例化和影响态度，通过开发基于语料库的方法来识别隐喻，因为它们出现在话语。目前，反污名化运动的效果主要是通过态度调查来衡量的（Rossetto等人，2019年）。态度调查在测量态度方面存在问题，因为参与者往往倾向于歪曲自己（例如，显得更有利），而且几乎不可能获得不太明显的态度，例如隐性耻辱（Stull等人，2013年）。间接衡量公众态度的一种方法是通过隐喻。从本质上讲，隐喻根据另一个域（源域）构建一个域（目标域），从而突出目标域中的某些方面，同时隐藏其他方面（Lakoff和约翰逊，1980）。在最近的工作中，这被描述为隐喻的“框架力量”（Semino，Demjen，Demmen，2016）。关于态度，这使得隐喻在调查通过语言表达的精神疾病的内隐概念化方面成为一个有力的工具。然而，要有效地研究公众态度，就需要大量的数据，这就需要发展基于语料库的方法。本研究的目的是：- 开发一种基于语料库的半自动方法，用于大规模识别和跟踪语篇中系统性隐喻的使用-将该方法应用于大型媒体语篇语料库，并在“变革时刻”运动之前和期间识别常见隐喻及其框架力量，在一个实验环境中评估主导框架和隐喻对话语参与者态度的影响。这个项目将涉及开发新的方法，用于基于语料库的隐喻识别和分析及其框架效应，并将其应用于新闻中对精神疾病的描述。它将有助于更好地了解英国精神疾病的语言结构如何随着时间的推移而变化，特别是在“改变的时间”反污名运动之前和期间。最终，这项研究可用于改善未来的反污名化运动，但也可用于根据公众的语言行为来衡量公众对各种问题的态度。

Stigmatizing attitudes towards mental illness pose a significant societal problem and its adverse effects permeate all areas of life for those affected. Among others, stigma leads to barriers in accessing care and a decreased likelihood of remaining in treatment (Corrigan et al. 2014), difficulty obtaining and retaining employment (Corrigan and Watson 2002) as well as increased social distance in community settings (Henderson et al. 2016). According to the Stigma Shout survey, 9 in 10 service users feel that stigmatization is negatively affecting their lives ('Time to Change', 2008). The largest anti-stigma campaign in England to date is the 'Time to Change' campaign. 'Time to Change' has so far operated in three phases (2007-2011; 2011-2015; 2016-2021) and focuses on de-stigmatizing mental. While this campaign has contributed to changing attitudes towards mental illness, in order to decrease stigma successfully and lastingly, there needs to be further research into how mental illness is constructed in public disocurse and in what ways certain discursive framings of mental illness contribute to different attitudes.In this project I will examine the language used to construct mental illness in mass media before and during the 'Time to Change' campaign, and how this language instantiates and affects attitudes, by developing corpus-based methods to identify metaphors as they emerge in discourse. Currently, the effect of anti-stigma campaigns is predominantly measured using attitude surveys (Rossetto et al., 2019). Attitude surveys are problematic for measuring attitudes because participants are often inclined to misrepresent themselves (e.g. to appear more favourably) and it is virtually impossible to access less overt attitudes, such as implicit stigma (Stull et al., 2013). One way to indirectly gauge public attitudes is through metaphor. Essentially, metaphors structure one domain (target domain) in terms of another (source domain), thereby highlighting certain aspects in the target domain, while hiding others (Lakoff and Johnson, 1980). In more recent work, this has been described as the 'framing power' of metaphor (Semino, Demjen, Demmen, 2016). Regarding attitudes, this makes metaphor a powerful tool in both investigating implicit conceptualizations of mental illness expressed through language. However, to study public attitudes effectively, it is necessary to draw on large amounts of data, which requires the development of corpus-based methods.The aims of this research are:- to develop to develop a corpus-based semi-automatic method for large-scale identification and tracking of systematic metaphor use in discourse- to apply this method to a large corpus of media discourse and identify common metaphors and their framing power before and during the phases of the 'Time to Change' campaign- to evaluate the effects of dominant frames and metaphors on attitudes of discourse participants in an experimental settingOverall, this project will involve the development of novel methods for the corpus-based identification and analysis of metaphors and their framing effects and apply this to the portrayal of mental illness in the news. It will contribute to a better understanding of how the linguistic construction of mental illness in the UK has changed over time, especially before and during the 'Time to Change' anti-stigma campaign. Ultimately, this research can be utilized to improve future anti-stigma campaigns, but also in general to gauge public attitudes towards various issues on basis of their language behaviour.