Speech is a powerful medium through which a variety of psychologically relevant phenomena are expressed. Here we take a first step in evaluating the potential of using voice samples as ...non-self-report measures of personality. In particular, we examine the extent to which linguistic and vocal information extracted from semi-structured vocal samples can be used to predict conventional measures of personality. We extracted 94 linguistic features (using Linquistic Inquiry Word Count, 2015) and 272 vocal features (using pyAudioAnalysis) from 614 voice samples of at least 50 words. Using a two-stage, fully automatable machine learning pipeline we evaluated the extent to which these features predicted self-report personality scales (Big Five Inventory). For comparison purposes, we also examined the predictive performance of these voice features with respect to depression, age, and gender. Results showed that voice samples accounted for 10.67 % of the variance in personality traits on average and that the same samples could also predict depression, age, and gender. Moreover, the results reported here provide a conservative estimate of the degree to which features derived from voice samples could be used to predict personality traits and suggest a number of opportunities to optimize personality prediction and better understand how voice samples carry information about personality.
•Short semi-structured voice samples predict self-reported personality and depression scores.•Linguistic and vocal features each have some unique predictive ability.•Predictiveness of voice samples vary across personality traits, with openness demonstrating weaker associations than others.•A fully automatible machine learning pipeline predicts all big five personality traits (Mean Out-Of-Sample R-squared 10.67%).
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
We find a consistent, unified narrative structure that can be reliably quantified using modern text analysis methods.
Scholars across disciplines have long debated the existence of a common structure ...that underlies narratives. Using computer-based language analysis methods, several structural and psychological categories of language were measured across ~40,000 traditional narratives (e.g., novels and movie scripts) and ~20,000 nontraditional narratives (science reporting in newspaper articles, TED talks, and Supreme Court opinions). Across traditional narratives, a consistent underlying story structure emerged that revealed three primary processes: staging, plot progression, and cognitive tension. No evidence emerged to indicate that adherence to normative story structures was related to the popularity of the story. Last, analysis of fact-driven texts revealed structures that differed from story-based narratives.
The huge power for social influence of digital media may come with the risk of intensifying common societal biases, such as gender and age stereotypes. Speaker's gender and age also behaviorally ...manifest in language use, and language may be a powerful tool to shape impact. The present study took the example of TED, a highly successful knowledge dissemination platform, to study online influence. Our goal was to investigate how gender- and age-linked language styles-beyond chronological age and identified gender-link to talk impact and whether this reflects gender and age stereotypes. In a pre-registered study, we collected transcripts of TED Talks along with their impact measures, i.e., views and ratios of positive and negative talk ratings, from the TED website. We scored TED Speakers' (N = 1,095) language with gender- and age-morphed language metrics to obtain measures of female versus male, and younger versus more senior language styles. Contrary to our expectations and to the literature on gender stereotypes, more female language was linked to higher impact in terms of quantity, i.e., more talk views, and this was particularly the case among talks with a lot of views. Regarding quality of impact, language signatures of gender and age predicted different types of positive and negative ratings above and beyond main effects of speaker's gender and age. The differences in ratings seem to reflect common stereotype contents of warmth (e.g., "beautiful" for female, "courageous" for female and senior language) versus competence (e.g., "ingenious", "informative" for male language). The results shed light on how verbal behavior may contribute to stereotypical evaluations. They also illuminate how, within new digital social contexts, female language might be uniquely rewarded and, thereby, an underappreciated but highly effective tool for social influence. WC = 286 (max. 300 words).
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Lifelong experiences and learned knowledge lead to shared expectations about how common situations tend to unfold. Such knowledge of narrative event flow enables people to weave together a story. ...However, comparable computational tools to evaluate the flow of events in narratives are limited. We quantify the differences between autobiographical and imagined stories by introducing sequentiality, a measure of narrative flow of events, drawing probabilistic inferences from a cutting-edge large language model (GPT-3). Sequentiality captures the flow of a narrative by comparing the probability of a sentence with and without its preceding story context. We applied our measure to study thousands of diary-like stories, collected from crowdworkers, about either a recent remembered experience or an imagined story on the same topic. The results show that imagined stories have higher sequentiality than autobiographical stories and that the sequentiality of autobiographical stories increases when the memories are retold several months later. In pursuit of deeper understandings of how sequentiality measures the flow of narratives, we explore proportions of major and minor events in story sentences, as annotated by crowdworkers. We find that lower sequentiality is associated with higher proportions of major events. The methods and results highlight opportunities to use cutting-edge computational analyses, such as sequentiality, on large corpora of matched imagined and autobiographical stories to investigate the influences of memory and reasoning on language generation processes.
The COVID-19 pandemic posed a global threat to nearly every society around the world. Individuals turned to their political leaders to safely guide them through this crisis. The most direct way ...political leaders communicated with their citizens was through official speeches and press conferences. In this report, we compare psychological language markers of four different heads of state during the early stage of the pandemic. Specifically, we collected all pandemic-related speeches and press conferences delivered by political leaders in the USA (Trump), UK (Johnson), Germany (Merkel), and Switzerland (Swiss Federal Council) between February 27th and August 31st, 2020. We used natural language analysis to examine language markers of expressed positive and negative emotions, references to the community (we-talk), analytical thinking, and authenticity and compare these language markers across the four nations. Level differences in the language markers between the leaders can be detected: Trump's language was characterized by a high expression of positive emotion, Merkel's by a strong communal focus, and Johnson's and the Swiss Federal Council by a high level of analytical thinking. Overall, these findings mirror different strategies used by political leaders to deal with the COVID-19 pandemic.
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Synchronized verbal behavior can reveal important information about social dynamics. This study introduces the linguistic style matching (LSM) algorithm for calculating verbal mimicry based on an ...automated textual analysis of function words. The LSM algorithm was applied to language generated during a small group discussion in which 70 groups comprised of 324 individuals engaged in an information search task either face-to-face or via text-based computer-mediated communication. As a metric, LSM predicted the cohesiveness of groups in both communication environments, and it predicted task performance in face-to-face groups. Other language features were also related to the groups’ cohesiveness and performance, including word count, pronoun patterns, and verb tense. The results reveal that this type of automated measure of verbal mimicry can be an objective, efficient, and unobtrusive tool for predicting underlying social dynamics. In total, the study demonstrates the effectiveness of using language to predict change in social psychological factors of interest.
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The ways we express ourselves in writing and speaking reveal who we are. Historically, most psychologists, social media experts, and even computer scientists have focused more on what people were ...saying rather than how they were saying what they were saying. Language content is, of course, critical to basic communication. Equally interesting is an analysis of common words associated with speaking style — words such as pronouns, prepositions, articles, and other function words. An increasing number of studies have found that function words (also thought of as stop words) provide clues to deception, status, intelligence, emotional state, the quality of social relationships, and personality. In addition to summarizing recent research on the social dynamics of language, the talk will point to the natural alliance of computer science and the field of social and personality psychology..
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Differences in the ways that men and women use language have long been of interest in the study of discourse. Despite extensive theorizing, actual empirical investigations have yet to converge on a ...coherent picture of gender differences in language. A significant reason is the lack of agreement over the best way to analyze language. In this research, gender differences in language use were examined using standardized categories to analyze a database of over 14,000 text files from 70 separate studies. Women used more words related to psychological and social processes. Men referred more to object properties and impersonal topics. Although these effects were largely consistent across different contexts, the pattern of variation suggests that gender differences are larger on tasks that place fewer constraints on language use.
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For the past decade, an increasing number of studies have demonstrated that when individuals write about emotional experiences, significant physical and mental health improvements follow. The basic ...paradigm and findings are summarized along with some boundary conditions. Although a reduction in inhibition may contribute to the disclosure phenomenon, changes in basic cognitive and linguistic processes during writing predict better health. Implications for theory and treatment are discussed.
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30.
Are Women Really More Talkative Than Men Mehl, Matthias R; Vazire, Simine; Ramírez-Esparza, Nairán ...
Science (American Association for the Advancement of Science),
07/2007, Volume:
317, Issue:
5834
Journal Article
Peer reviewed
Women are generally assumed to be more talkative than men. Data were analyzed from 396 participants who wore a voice recorder that sampled ambient sounds for several days. Participants' daily word ...use was extrapolated from the number of recorded words. Women and men both spoke about 16,000 words per day.
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