Modern authorship attribution methods are often comprised of powerful yet opaque machine learning algorithms. While much of this work lends itself to concrete outcomes in the form of probability ...scores, advanced approaches typically preclude deeper insights in the form of psychological interpretation. Additionally, few attribution methods exist for single-candidate authorship problems, most of which require large amounts of supplemental data to perform and none of which rely upon explicitly psychological measures. The current study introduces Mental Profile Mapping, a new authorship attribution technique for single-candidate authorship questions that is founded on previous scientific research pertaining to the nature of language and psychology. In the current study, baseline expectations for results and performance are set using an advanced technique known as "unmasking" on the test case of Aphra Behn, a 17th century English playwright. Following this, Mental Profile Mapping is introduced and tested for its psychometric properties, tested using a "bogus insertion" method, and then applied to canonical Aphra Behn plays. Results from both attribution methods suggest that 2 of 5 questioned plays are likely to have been authored by Behn, with the remaining 3 plays exhibiting a poor fit for Behn's psychological fingerprint. Mental Profile Mapping results are then decomposed into deeper psychological interpretation, a quality unique to this new method.
•Psychologists have known for years that self-reports suffer from serious limitations.•Linguistic measures of personality have proven to be extremely effective.•Estimation of self-reports using ...language is currently popular, yet often misguided.•Language analysis for personality research is uniquely scalable to big data.
Personality is typically defined as the consistent set of traits, attitudes, emotions, and behaviors that people have. For several decades, a majority of researchers have tacitly agreed that the gold standard for measuring personality was with self-report questionnaires. Surveys are fast, inexpensive, and display beautiful psychometric properties. A considerable problem with this method, however, is that self-reports reflect only one aspect of personality—people's explicit theories of what they think they are like. We propose a complementary model that draws on a big data solution: the analysis of the words people use. Language use is relatively reliable over time, internally consistent, and differs considerably between people. Language-based measures of personality can be useful for capturing/modeling lower-level personality processes that are more closely associated with important objective behavioral outcomes than traditional personality measures. Additionally, the increasing availability of language data and advances in both statistical methods and technological power are rapidly creating new opportunities for the study of personality at ‘big data’ scale. Such opportunities allow researchers to not only better understand the fundamental nature of personality, but at a scale never before imagined in psychological research.
To date we know little about natural emotion word repertoires, and whether or how they are associated with emotional functioning. Principles from linguistics suggest that the richness or diversity of ...individuals' actively used emotion vocabularies may correspond with their typical emotion experiences. The current investigation measures active emotion vocabularies in participant-generated natural speech and examined their relationships to individual differences in mood, personality, and physical and emotional well-being. Study 1 analyzes stream-of-consciousness essays by 1,567 college students. Study 2 analyzes public blogs written by over 35,000 individuals. The studies yield consistent findings that emotion vocabulary richness corresponds broadly with experience. Larger negative emotion vocabularies correlate with more psychological distress and poorer physical health. Larger positive emotion vocabularies correlate with higher well-being and better physical health. Findings support theories linking language use and development with lived experience and may have future clinical implications pending further research.
Throughout history, scholars and laypeople alike have believed that our words contain subtle clues about what we are like as people, psychologically speaking. However, the ways in which language has ...been used to infer psychological processes has seen dramatic shifts over time and, with modern computational technologies and digital data sources, we are on the verge of a massive revolution in language analysis research. In this article, we discuss the past and current states of research at the intersection of language analysis and psychology, summarizing the central successes and shortcomings of psychological text analysis to date. We additionally outline and discuss a critical need for language analysis practitioners in the social sciences to expand their view of verbal behavior. Lastly, we discuss the trajectory of interdisciplinary research on language and the challenges of integrating analysis methods across paradigms, recommending promising future directions for the field along the way.
From many perspectives, the election of Donald Trump was seen as a departure from long-standing political norms. An analysis of Trump’s word use in the presidential debates and speeches indicated ...that he was exceptionally informal but at the same time, spoke with a sense of certainty. Indeed, he is lower in analytic thinking and higher in confidence than almost any previous American president. Closer analyses of linguistic trends of presidential language indicate that Trump’s language is consistent with long-term linear trends, demonstrating that he is not as much an outlier as he initially seems. Across multiple corpora from the American presidents, non-US leaders, and legislative bodies spanning decades, there has been a general decline in analytic thinking and a rise in confidence in most political contexts, with the largest and most consistent changes found in the American presidency. The results suggest that certain aspects of the language style of Donald Trump and other recent leaders reflect long-evolving political trends. Implications of the changing nature of popular elections and the role of media are discussed.
Despite the established health and ecological benefits of a plant-based diet, the decision to eschew meat and other animal-derived food products remains controversial. So polarising is this topic ...that anti-vegan communities — groups of individuals who stand vehemently against veganism — have sprung up across the internet. Much scholarship on veganism characterizes anti-vegans in passing, painting them as ill-informed, uneducated, or simply obstinate. However, little empirical work has investigated these communities and the individuals within them. Accordingly, we conducted a study using social media data from the popular platform, Reddit. Specifically, we collected all available submissions (∼3523) and comments (∼45,528) from r/AntiVegan subreddit users (N = 3819) over a five-year period. Using a battery of computerized text analytic tools, we examined the psychosocial characteristics of Reddit users who publicly identify as anti-vegan, how r/AntiVegan users discuss their beliefs, and how the individual user changes as a function of community membership. Results from our analyses suggest several individual differences that align r/AntiVegan users with the community, including dark entertainment, ex-veganism and science denial. Several topics were extensively discussed by r/AntiVegan members, including nuanced discourse on the ethicality and health implications of vegan diets, and the naturalness of animal death, which ran counter to our expectations and lay stereotypes of r/AntiVegan users. Finally, several longitudinal changes in language use were observed within the community, reflecting enhanced group commitment over time, including an increase in group-focused language and a decrease in cognitive processing. Implications for vegan-nonvegan relations are discussed.
More than 100 years after Shakespeare's death, Lewis Theobald published Double Falsehood, a play supposedly sourced from a lost play by Shakespeare and John Fletcher. Since its release, scholars have ...attempted to determine its true authorship. Using new approaches to language and psychological analysis, we examined Double Falsehood and the works of Theobald, Shakespeare, and Fletcher. Specifically, we created a psychological signature from each author's language and statistically compared the features of each signature with those of Double Falsehood's signature. Multiple analytic approaches converged in suggesting that Double Falsehood's psychological style and content architecture predominantly resemble those of Shakespeare, showing some similarity with Fletcher's signature and only traces of Theobald's. Closer inspection revealed that Shakespeare's influence is most apparent early in the play, whereas Fletcher's is most apparent in later acts. Double Falsehood has a psychological signature consistent with that expected to be present in the long-lost play The History of Cardenio, cowritten by Shakespeare and Fletcher.
Mental health (MH) peer online forums offer robust support where internet access is common, but healthcare is not, e.g., in countries with under-resourced MH support, rural areas, and during ...pandemics. Despite their widespread use, little is known about who posts in such forums, and in what mood states. The discussion platform Reddit is ideally suited to study this as it hosts forums (subreddits) for MH and non-MH topics. In bipolar disorder (BD), where extreme mood states are core defining features, mood influences are particularly relevant. This exploratory study investigated posting patterns of Reddit users with a self-reported BD diagnosis and the associations between posting and emotions, specifically: 1) What proportion of the identified users posts in MH versus non-MH subreddits? 2) What differences exist in the emotions that they express in MH or non-MH subreddit posts? 3) How does mood differ between those users who post in MH subreddits compared to those who only post in non-MH subreddits? Reddit users were automatically identified via self-reported BD diagnosis statements and all their 2005-2019 posts were downloaded. First, the percentages of users who posted only in MH (non-MH) subreddits were calculated. Second, affective vocabulary use was compared in MH versus non-MH subreddits by measuring the frequency of words associated with positive emotions, anxiety, sadness, anger, and first-person singular pronouns via the LIWC text analysis tool. Third, a logistic regression distinguished users who did versus did not post in MH subreddits, using the same LIWC variables (measured from users' non-MH subreddit posts) as predictors, controlling for age, gender, active days, and mean posts/day. 1) Two thirds of the identified 19,685 users with a self-reported BD diagnosis posted in both MH and non-MH subreddits. 2) Users who posted in both MH and non-MH subreddits exhibited less positive emotion but more anxiety and sadness and used more first-person singular pronouns in their MH subreddit posts. 3) Feminine gender, higher positive emotion, anxiety, and sadness were significantly associated with posting in MH subreddits. Many Reddit users who disclose a BD diagnosis use a single account to discuss MH and other concerns. Future work should determine whether users exhibit more anxiety and sadness in their MH subreddit posts because they more readily post in MH subreddits when experiencing lower mood or because they feel more able to express negative emotions in these spaces. MH forums may reflect the views of people who experience more extreme mood (outside of MH subreddits) compared to people who do not post in MH subreddits. These findings can be useful for MH professionals to discuss online forums with their clients. For example, they may caution them that forums may underrepresent people living well with BD.
Natural language processing (NLP)-previously the domain of a select few language and computer scientists-is undergoing an unprecedented surge in popularity across disciplines. The ubiquity of ...language data, alongside extremely rapid methodological innovations, has magnetized the field, attracting researchers with the promise of measuring, forecasting, and understanding the most central questions in business, psychology, biology, sociology, the humanities, and beyond. The power of language analysis to reveal insights into human thought, feeling, and behavior has become a core interest emerging from recent technological advances, which are being probed to unearth deeply embedded truths about the human condition. However, NLP research has reached a critical juncture, sitting at the cusp of societal transformation in many aspects of daily life. The details of how NLP research develops over the next 3-5 years will define this transformation. In this emerging, near-infinite space of NLP-driven research, we provide a critical frame of reference for how, when, and why these technologies should evolve in a particularly transdisciplinary manner. Specifically, we discuss (a) the urgency of pairing existing and emerging NLP research with existing scientific knowledge, theory, and principles from the behavioral sciences; (b) the coevolution of NLP technologies; and (c) the practical implications and ethical consequences of expanding language analysis using broader psychosocial theories of the human condition. While our discussion focuses principally on using language as a window in the
this topic holds substantial implications for other disciplines and lines of inquiry, including the dynamics of social interaction and beyond. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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