Technology now makes it possible to understand efficiently and at large scale how people use language to reveal their everyday thoughts, behaviors, and emotions. Written text has been analyzed through both theory-based, closed-vocabulary methods from the social sciences as well as data-driven, open-vocabulary methods from computer science, but these approaches have not been comprehensively compared. To provide guidance on best practices for automatically analyzing written text, this narrative review and quantitative synthesis compares five predominant closed- and open-vocabulary methods: Linguistic Inquiry and Word Count (LIWC), the General Inquirer, DICTION, Latent Dirichlet Allocation, and Differential Language Analysis. We compare the linguistic features associated with gender, age, and personality across the five methods using an existing dataset of Facebook status updates and self-reported survey data from 65,896 users. Results are fairly consistent across methods. The closed-vocabulary approaches efficiently summarize concepts and are helpful for understanding how people think, with LIWC2015 yielding the strongest, most parsimonious results. Open-vocabulary approaches reveal more specific and concrete patterns across a broad range of content domains, better address ambiguous word senses, and are less prone to misinterpretation, suggesting that they are well-suited for capturing the nuances of everyday psychological processes. We detail several errors that can occur in closed-vocabulary analyses, the impact of sample size, number of words per user and number of topics included in open-vocabulary analyses, and implications of different analytical decisions. We conclude with recommendations for researchers, advocating for a complementary approach that combines closed- and open-vocabulary methods.

Technology now makes it possible to understand efficiently and at large scale how people use language to reveal their everyday thoughts, behaviors, and emotions. Written text has been analyzed through both theory-based, closed-vocabulary methods from the social sciences as well as data-driven, open-vocabulary methods from computer science, but these approaches have not been comprehensively compared. To provide guidance on best practices for automatically analyzing written text, this narrative review and quantitative synthesis compares five predominant closed- and open-vocabulary methods: Linguistic Inquiry and Word Count (LIWC), the General Inquirer, DICTION, Latent Dirichlet Allocation, and Differential Language Analysis. We compare the linguistic features associated with gender, age, and personality across the five methods using an existing dataset of Facebook status updates and self-reported survey data from 65,896 users. Results are fairly consistent across methods. The closed-vocabulary approaches efficiently summarize concepts and are helpful for understanding how people think, with LIWC 2015 yielding the strongest, most parsimonious results. Open-vocabulary approaches reveal more specific and concrete patterns across a broad range of content domains, better address ambiguous word senses, and are less prone to misinterpretation, suggesting that they are well-suited for capturing the nuances of everyday psychological processes. We detail several errors that can occur in closed-vocabulary analyses, the impact of sample size, number of words per user and number of topics included in open-vocabulary analyses, and implications of different analytical decisions. We conclude with recommendations for researchers, advocating for a complementary approach that combines closed- and open-vocabulary methods.

Everyday beliefs often organize and guide motivations, goals, and behaviors, and, as such, may also differentially motivate individuals to value and attend to emotion-related cues of others. In this way, the beliefs that individuals hold may affect the socioemotional skills that they develop. To test the role of emotion-related beliefs specific to anger, we examined an educational context in which beliefs could vary and have implications for individuals' skill. Specifically, we studied 43 teachers' beliefs about students' anger in the school setting as well as their ability to recognize expressions of anger in children's faces in a dynamic emotion recognition task. Results revealed that, even when controlling for teachers' age and gender, teachers' belief that children's anger was useful and valuable in the school setting was associated with teachers' accuracy at recognizing anger expressions in children's faces. The belief that children's anger was harmful and not conducive to learning, however, was not associated with teachers' accuracy at recognizing children's anger expressions. These findings suggest that certain everyday beliefs matter for predicting skill in recognizing specific emotion-related cues.

White parents’ approaches to racial socialization can have significant consequences for children’s understanding of race, racial bias, and racial justice. Across three studies, we attempted to identify three racial socialization practices that White parents employ. In Study 1, 238 White parents self-reported their racial socialization practices and listed their children’s friends’ age, race, and gender. Exploratory factor analysis suggested evidence for three scales: race-consciousness, discussion-hesitancy, and race-evasiveness. Parents’ discussion hesitancy was positively associated, and race consciousness negatively associated, with the racial homogeneity of their child’s friendship group. In Study 2 (N = 79), White parents’ discussion-hesitancy was again positively associated with the racial homogeneity of their child’s friendship group. In Study 3, with 21 White parents and their children independently reporting, White parents’ discussion hesitancy was again positively associated with the racial homogeneity of their child’s friendship group. Parents’ comfort level when discussing race and parents’ intergroup contact provided additional validational evidence.

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