Real Men Don’t Say “Cute”

Carpenter, Jordan; Preoţiuc-Pietro, Daniel; Flek, Lucie; Giorgi, Salvatore; Hagan, Courtney A.; Buffone, Anneke; Ungar, Lyle H.; Seligman, Martin E. P.

doi:10.1177/1948550616671998

Cited by 17 publications

(4 citation statements)

References 36 publications

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“…For example, well-established social biases that can be found in studies of "implicit associations" have recently been shown to have analogues in how social concepts are represented in word embedding models (Bolukbasi et al, 2016;Caliskan et al, 2017). Similarly, the changing social landscape can be seen in the rapid increase of research into stereotypes, prejudices, and biases as they are formulated, embedded, and surreptitiously transmitted in our language (Carpenter et al, 2017;Snefjella et al, 2018). We are excited to see what can be learned about ourselves through the study of increasingly veridical language models created by highly collaborative teams of scholars spanning the sciences.…”

Section: Embeddings and Beyondmentioning

confidence: 99%

Natural Language Analysis and the Psychology of Verbal Behavior: The Past, Present, and Future States of the Field

Boyd

Schwartz

2020

Journal of Language and Social Psychology

195

147

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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.

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Section: Embeddings and Beyondmentioning

confidence: 99%

Natural Language Analysis and the Psychology of Verbal Behavior: The Past, Present, and Future States of the Field

Boyd

Schwartz

2020

Journal of Language and Social Psychology

195

147

View full text Add to dashboard Cite

show abstract

“…The dictionaries contain weighted lists of words that have previously been successful in predicting authors' gender and age from text. Both lexica have been widely used in research on social media [63][64][65][66], and the gender lexicon has been shown to achieve 91.9% prediction accuracy in determining gender from language [12].…”

Section: Methodsmentioning

confidence: 99%

Stereotyping in the digital age: Male language is “ingenious”, female language is “beautiful” – and popular

et al. 2020

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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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“…An individual's consciousness does not control implicit bias. The primary purpose of measuring implicit bias is to predict behaviors that explicitly held biases cannot predict [49]. For example, although one deliberately claims to prefer African Americans to European Americans, he/she may associate African Americans with negativity in implicit cognition [5].…”

Section: Future Workmentioning

confidence: 99%

Automatically Distinguishing People’s Explicit and Implicit Attitude Bias by Bridging Psychological Measurements with Sentiment Analysis on Large Corpora

Wang,

Zhang,

Xue

et al. 2024

Applied Sciences

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Social psychological studies show that people’s explicit attitude bias in public expression can differ from their implicit attitude bias in mind. However, the current automatic attitude analysis does not distinguish between explicit and implicit attitude bias. Simulating the psychological measurements of explicit and implicit attitude bias, i.e., self-report assessment (SRA) and implicit association test (IAT), we propose an automatic language-based analysis to distinguish explicit and implicit attitude bias in a large population. By connecting the criteria of SRA and IAT with the statements containing patterns of special words, we derive explicit and implicit attitude bias with the sentiment scores of the statements, which are obtained by pre-trained machine-learning methods. Extensive experiments on four English and Chinese corpora and four pairs of concepts show that the attitude biases obtained by our method on a large population are consistent with those of traditional psychological experiments in the costly small-scale experiments. The maximum gap between the sentiment scores of explicit and implicit biases reaches 0.9329. Furthermore, we achieve new findings on the difference between the evolution of explicit and implicit attitude bias. The maximum variance gap of sentiment scores in the dynamic changes between explicit and implicit biases reaches 0.249.

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Real Men Don’t Say “Cute”

Cited by 17 publications

References 36 publications

Natural Language Analysis and the Psychology of Verbal Behavior: The Past, Present, and Future States of the Field

Natural Language Analysis and the Psychology of Verbal Behavior: The Past, Present, and Future States of the Field

Stereotyping in the digital age: Male language is “ingenious”, female language is “beautiful” – and popular

Automatically Distinguishing People’s Explicit and Implicit Attitude Bias by Bridging Psychological Measurements with Sentiment Analysis on Large Corpora

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