129 research outputs found

    Personality Dysfunction Manifest in Words : Understanding Personality Pathology Using Computational Language Analysis

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    Personality disorders (PDs) are some of the most prevalent and high-risk mental health conditions, and yet remain poorly understood. Today, the development of new technologies means that there are advanced tools that can be used to improve our understanding and treatment of PD. One promising tool – indeed, the focus of this thesis – is computational language analysis. By looking at patterns in how people with personality pathology use words, it is possible to gain access into their constellation of thinking, feelings, and behaviours. To date, however, there has been little research at the intersection of verbal behaviour and personality pathology. Accordingly, the central goal of this thesis is to demonstrate how PD can be better understood through the analysis of natural language. This thesis presents three research articles, comprising four empirical studies, that each leverage computational language analysis to better understand personality pathology. Each paper focuses on a distinct core feature of PD, while incorporating language analysis methods: Paper 1 (Study 1) focuses on interpersonal dysfunction; Paper 2 (Studies 2 and 3) focuses on emotion dysregulation; and Paper 3 (Study 4) focuses on behavioural dysregulation (i.e., engagement in suicidality and deliberate self-harm). Findings from this research have generated better understanding of fundamental features of PD, including insight into characterising dimensions of social dysfunction (Paper 1), maladaptive emotion processes that may contribute to emotion dysregulation (Paper 2), and psychosocial dynamics relating to suicidality and deliberate self-harm (Paper 3) in PD. Such theoretical knowledge subsequently has important implications for clinical practice, particularly regarding the potential to inform psychological therapy. More broadly, this research highlights how language can provide implicit and unobtrusive insight into the personality and psychological processes that underlie personality pathology at a large-scale, using an individualised, naturalistic approach

    Cognitive network science reveals bias in GPT-3, ChatGPT, and GPT-4 mirroring math anxiety in high-school students

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    Large language models are becoming increasingly integrated into our lives. Hence, it is important to understand the biases present in their outputs in order to avoid perpetuating harmful stereotypes, which originate in our own flawed ways of thinking. This challenge requires developing new benchmarks and methods for quantifying affective and semantic bias, keeping in mind that LLMs act as psycho-social mirrors that reflect the views and tendencies that are prevalent in society. One such tendency that has harmful negative effects is the global phenomenon of anxiety toward math and STEM subjects. Here, we investigate perceptions of math and STEM fields provided by cutting-edge language models, namely GPT-3, Chat-GPT, and GPT-4, by applying an approach from network science and cognitive psychology. Specifically, we use behavioral forma mentis networks (BFMNs) to understand how these LLMs frame math and STEM disciplines in relation to other concepts. We use data obtained by probing the three LLMs in a language generation task that has previously been applied to humans. Our findings indicate that LLMs have an overall negative perception of math and STEM fields, with math being perceived most negatively. We observe significant differences across the three LLMs. We observe that newer versions (i.e. GPT-4) produce richer, more complex perceptions as well as less negative perceptions compared to older versions and N=159 high-school students. These findings suggest that advances in the architecture of LLMs may lead to increasingly less biased models that could even perhaps someday aid in reducing harmful stereotypes in society rather than perpetuating them.Comment: 23 pages, 8 figure

    COVID-19 Booster Vaccine Acceptance in Ethnic Minority Individuals in the United Kingdom: a mixed-methods study using Protection Motivation Theory

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    Background: Uptake of the COVID-19 booster vaccine among ethnic minority individuals has been lower than in the general population. However, there is little research examining the psychosocial factors that contribute to COVID-19 booster vaccine hesitancy in this population.Aim: Our study aimed to determine which factors predicted COVID-19 vaccination intention in minority ethnic individuals in Middlesbrough, using Protection Motivation Theory (PMT) and COVID-19 conspiracy beliefs, in addition to demographic variables.Method: We used a mixed-methods approach. Quantitative data were collected using an online survey. Qualitative data were collected using semi-structured interviews. 64 minority ethnic individuals (33 females, 31 males; mage = 31.06, SD = 8.36) completed the survey assessing PMT constructs, COVID-19conspiracy beliefs and demographic factors. 42.2% had received the booster vaccine, 57.6% had not. 16 survey respondents were interviewed online to gain further insight into factors affecting booster vaccineacceptance.Results: Multiple regression analysis showed that perceived susceptibility to COVID-19 was a significant predictor of booster vaccination intention, with higher perceived susceptibility being associated with higher intention to get the booster. Additionally, COVID-19 conspiracy beliefs significantly predictedintention to get the booster vaccine, with higher conspiracy beliefs being associated with lower intention to get the booster dose. Thematic analysis of the interview data showed that barriers to COVID-19 booster vaccination included time constraints and a perceived lack of practical support in the event ofexperiencing side effects. Furthermore, there was a lack of confidence in the vaccine, with individuals seeing it as lacking sufficient research. Participants also spoke of medical mistrust due to historical events involving medical experimentation on minority ethnic individuals.Conclusion: PMT and conspiracy beliefs predict COVID-19 booster vaccination in minority ethnic individuals. To help increase vaccine uptake, community leaders need to be involved in addressing people’s concerns, misassumptions, and lack of confidence in COVID-19 vaccination

    Challenges and perspectives of hate speech research

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    This book is the result of a conference that could not take place. It is a collection of 26 texts that address and discuss the latest developments in international hate speech research from a wide range of disciplinary perspectives. This includes case studies from Brazil, Lebanon, Poland, Nigeria, and India, theoretical introductions to the concepts of hate speech, dangerous speech, incivility, toxicity, extreme speech, and dark participation, as well as reflections on methodological challenges such as scraping, annotation, datafication, implicity, explainability, and machine learning. As such, it provides a much-needed forum for cross-national and cross-disciplinary conversations in what is currently a very vibrant field of research

    Effects of Context(s) on Political Radicalisation

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    Understanding the drivers of political radicalisation is necessary to predict and plan for radicalised responses. While the radicalisation literature shows an increasing interest in the ways context elicits radicalised behaviours, empirical research in this area remains limited. Additionally, while this literature categorises context into individual, group, and mass level, it has rarely systematically tested how a combination of these categories affect radical behavioural outcomes. In this thesis, I argue that accounting for the interdependency between different context categories can explain the heterogeneity of radicalisation processes and outcomes. I draw on contextual challenges that are prevalent in our social reality to examine how individuals’ online/offline societal experiences, alongside broader categories of socio-political contexts and national cultural references, drive radical endorsements. More specifically, I use this context interdependency to examine both radical shifts and the underlying processes that direct these shifts. In doing so, I propose a conceptual framework which identifies the biopsychosocial mechanisms that are likely to stimulate radical action (Chapter 1). A contextual approach to political radicalisation assumes that different sets of context categories interact in diverse ways and are likely to instigate psychological processes that drive different forms of radical outcomes. To investigate this assumption, I explored how context interdependency affects physio-cognitive and group processes to elicit support for radical actions. Using big data (Google search data) and two different experimental designs (with the general population and students from the UK and USA), I showed how combining online societal experiences and socio-cultural contexts predicts radical shifts in response to practices of surveillance and privacy violation over time (Chapter 2). Extending this research, five experiments were carried out to show that radical endorsement-as measured with response to hate speech, Brexit, vote denials in European elections, and climate change- are predicted by a combination of online/offline societal experiences, socio-political and national cultural contexts and determined by physio-cognitive processes, identity processes and individual belief systems (Chapter 3). Theoretical implications for the importance of context in shaping political radicalisation and practical implications for explaining radical shifts are provided (Chapter 4)

    Factors influencing variation in face processing

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    Perceptions of faces, such as judgments about others’ emotional states or attractiveness from facial characteristics, influence social interaction. However, relatively few studies have investigated factors that predict variation among individuals or groups of individuals in how people perceive facial characteristics and results from studies on this topic that have been reported have often been subsequently shown to not be robust. For these reasons, the studies reported in this thesis investigated potential sources of variation in face perception, focusing on (1) the relationship between affective factors and perceptions of facial expression of emotion in a UK sample and (2) the effects of sexually dimorphic face-shape characteristics on social judgments of faces in samples of Arab women. Chapter 2 (the first empirical chapter) reports results from a Registered Report investigating relationships between different affective factors and emotion perception. Results replicated previous studies suggesting that participants scoring higher on generalised anxiety performed poorer on emotion perception tasks, but also found evidence that other affective factors, particularly those related to empathy, also contributed to variation in emotion perception. While Chapter 2 had investigated responses to facial characteristics that can change very rapidly (emotional expressions), Chapters 3 and 4 investigated Arab women’s responses to a facial characteristic that is relatively stable over time (sexually dimorphic face-shape characteristics). Results from this series of studies suggested that Arab women perceived feminised versions of men’s faces to be more attractive, younger-looking, and less dominant than masculinised versions, but found no effects of sexually dimorphic face-shape characteristics on perceptions of men’s trustworthiness or health. These results for Arab women’s face perceptions show some similarities (e.g., femininized faces look more attractive, younger, and less dominant than masculinised faces) to results previously reported for UK women’s face perceptions, but also show some differences (e.g., UK women typically find feminized faces look more trustworthy, a pattern not seen in this sample of Arab women). Together, the results reported in this thesis suggest that affective factors and cultural differences may contribute to variation in face perceptions and highlight the importance of considering variation when studying face perception

    Who Blames Female Victims of Revenge Pornography?

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    Revenge pornography refers to any kind of uploading or publishing private photos or videos of someone without their consent. The public can often blame female victims of revenge pornography for engaging in the risky behaviour of taking nude pictures or videos in the first place. Certain individual and socio-demographic characteristics of the public can lead to victim-blaming. We wanted to see if characteristics relevant in the context of blaming rape victims for their victimisation, such as ambivalent sexism, moral foundations, conservatism, age and gender, contribute to blaming victims of revenge pornography. Convenient sample consisted of N = 364 participants (73.3% women), with an average age of 38.07 (SD = 13.74), and slightly more socio-liberal orientation, according to self-ssessment (a broad social attitudes 7-point scale ranging from 1-liberal to 7-conservative (M = 2.97, SD = 1.49)). In an online survey, participants were presented with a vignette describing a bogus case of a woman whose pictures a man posted on the internet. The participant's task was to assess who should take responsibility for this event on a 7-point scale, ranging from 1, meaning the woman, through 4, meaning both the woman and the man equally, to 7, meaning the man. The distribution of answers was trimodal (on word anchors) and negatively asymmetric because 52.2% of participants said that the man should take responsibility. After attributing responsibility, participants filled out the Ambivalent sexism inventory with 22 items (α = .91) and the Moral foundations questionnaire with 30 items (all five subscales, α = .66-.81). Regression model with sociodemographics, together with ambivalent sexism and moral progressivity, explained 19.8% of the variance in victim-blaming (F(5, 354) = 17.22, p < .001). Ambivalent sexism (β = -0.27, p < .001) contributed the most, followed by moral progressivity (β = 0.17, p = .01), while gender, age, and conservatism were not significant predictors. Content analysis of ambivalent sexist attitudes and less progressive moral foundations can help us create a substitute for the victim-blaming narrative around victims of revenge pornography which would still fit the mindset of current victim-blamers (e. g. “women take and share their private photos or videos to special men in their life to please them”). Besides the practical application, the study's findings contribute to the ongoing debate over the theoretical soundness of Moral foundations theory because holding less progressive moral foundations, which are exclusively proposed by this theory, leads to an apology for violence

    Uncertainty, risk, and financial disclosures : applications of natural language processing in behavioral economics

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    In the last decade, natural language processing (NLP) methods have received increasing attention for applications in behavioral economics. Such methods enable the automatic content analysis of large corpora of financial disclosures, e.g., annual reports or earnings calls. In this setting, a conceptually interesting but underexplored variable is linguistic uncertainty: Due to the unpredictability of the financial market, it is often necessary for corporate management to use hedge expressions such as “likely” or “possible” in their financial communication. On the other hand, management can also use uncertain language to influence investors strategically, for example, through deliberate obfuscation. In this dissertation, we present NLP methods for the automated detection of linguistic uncertainty. Furthermore, we introduce the first experimental study to establish a causal link between linguistic uncertainty and investor behavior. Finally, we propose regression models to explain and predict financial risk. In addition to the independent variable of linguistic uncertainty, we explore a psychometric and an assumption-free model based on Deep Learning
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