Subjects: Psychology >> Applied Psychology Subjects: Computer Science >> Computer Application Technology submitted time 2024-03-23
Abstract： In recent years, researchers have recognized the impact of Zhong-yong Thinking Style on mental health. However, it is not clear how Zhong-yong thinking style affects mental health through internal psychological mechanisms. Previous studies found that individuals with a better ability to coordinate agency (a motivation representing self-interest) and communion (a motivation representing altruism) tend to have a higher level of moral centrality. Moral centrality reflects the balance of internal motivation system, which can reduce the conflict between agency and communion, helping individuals reach a state that the opposing motivations support and energies each other. Moral centrality may play a potential mediating role in the impact of Zhong-yong thinking style on mental health. Although there are relatively mature methods for measuring individual moral centrality, it involves the complex task of coding values in personal strivings, making the measurement of moral centrality particularly complicated and labor-intensive. However, with the development of large language models(LLM) like ChatGPT, they have demonstrated excellent contextual comprehension skills and offered new possibilities for text analysis and coding work. Accordingly, this study intends to apply large language models to the coding work of psychological research, reduce the time and labor cost required in the process of measuring individual moral centrality, and explore how Zhong-yong thinking style affects individual mental health through moral centrality. Study 1 involves training GPT-3.5 Turbo to recognize values contained in personal strivings (achievement / power / universalism / benevolence) using differentiated prompts and evaluating its accuracy, precision, and recall rates, in order to obtain a model that meets the requirements for application. Study 2 applies above GPT-3.5 Turbo models in the process of measuring moral centrality, exploring how moral centrality mediates the impact of Zhong-yong thinking style on depression and anxiety. The findings are as follows: (1) The GPT-3.5 Turbo demonstrated an accuracy rate of not less than 0.80 in recognizing values of power, achievement, universlaism, and benevolence, showing the potential application of ChatGPT in psychological research; (2) Moral centrality played a mediating role in the impact of Zhong-yong thinking style on depression/anxiety. Specifically, individuals with a higher level of Zhong-yong thinking style could better integrate agency and communion, enhancing their moral centrality, and thereby reducing levels of depression/anxiety. In summary, this study utilized large language models to break through the technical limitations of traditional psychological research, exploring the mechanisms through which Zhong-yong thinking style affects mental health and verifying the mediating role of moral centrality. On the one hand, it proves the application potential of large language models in the field of psychological research. On the other hand, it deepens our understanding of the mechanisms through which Zhong-yong thinking style influence mental health, enriching the theoretical foundation of this field. It suggests that policymakers could use the advantages of Zhongyong thinking culture, advocating for values that emphasize individual development while also focusing on collective well-being, helping people improve moral centrality, thereby mitigating the negative impact of economic inequality on mental health.
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Subjects: Psychology >> Applied Psychology Subjects: Computer Science >> Computer Application Technology submitted time 2024-03-23
Abstract： In recent years, researchers have increasingly recognized the impact of unequal income distribution on individual mental health. However, it is not clear how it affects mental health through internal psychological mechanisms. As the macro environment in which individuals live, economy shape people’s different values and make individuals have different levels of motivation orientation. Previous studies have indicated that individuals with a better ability to coordinate agency and communion tend to have a relatively high level of moral centrality. Moral centrality reflects the balance of internal motivation system, which can reduce the conflict between agency and communion, helping individuals reach a state that the opposing motivations support and energies each other. Thus, individuals are not only able to efficiently realize their personal values but also more easily allow for the attainment of eudaimonic well-being, thereby reducing the risk of mental health problems. Therefore, moral centrality may play a potential mediating role in the impact of income distribution inequality on mental health. Overall, with income distribution inequality as independent variables, this study aims to explore the mechanisms through which it affects mental health, by examining how income distribution influences individual moral centrality and, in turn, affect mental health. Our research not only enriches the theoretical foundation of the mental health field, but also provides a theoretical basis for interventions, and helps to formulate targeted strategies to improve the psychological well-being of the public. With the help of social media big data and natural language processing technology, we use posts made by regional microblogs to extract word frequency features representing the group’s moral centrality and group’s mental health level through the psychosemantic lexicon, and use panel data analysis to examine how the inequality in income distribution affects the negative emotions and suicide risk of the regional group through moral centrality. The results confirm that moral centrality plays a mediating role in the effect of regional income distribution inequality on group negative emotions/suicide risk, and that regions with higher income distribution inequality tend to be accompanied by lower levels of group moral centrality, which in turn leads to an increase in negative emotions/suicide risk among groups in the region.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Applied Psychology submitted time 2022-03-18
Abstract：
This paper introduces the big data research method in psychology in details, taking Ninety-Nine Articles website as an example. Using the collected textual data, we calculated word frequencies as features, then trained machine learning models, and used models to predict life satisfaction for texts crawled from Ninety-Nine Articles website, providing inspiration and help for beginners in big data research. This paper introduces text-based word frequency calculation using Python and sentiment dictionary through specific examples, and completes the training, testing and application of the machine learning model using Python's scikit-learn library. Furthermore, we uploaded the accompanying source program for direct operation. This paper introduces the big data research method of machine learning modeling via text-based word frequency. Our article emphasizes how to apply the technology, and thus we introduce the technology in a more basic way with less involvement of the technical principles.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Applied Psychology submitted time 2022-03-06
Abstract：
[Objective] This study is based on the Tianya community and explores the differences in parents' life satisfaction and emotional state when children at different educational stages. [Methods] The word frequency distribution of parents whose children are in preschool, primary school and junior middle school is calculated by using the Emotion Dictionary of Dalian Institute of Technology, and the life satisfaction of the parents is predicted based on the word frequency. We then compare the differences in parents' life satisfaction and emotional state between groups. [Results] For life satisfaction, junior high school parents were significantly lower than preschool parents and primary school parents. The result indicated that in terms of happy emotional words, the word frequency of pre-school parents was higher than that of primary and junior middle school parents. While in terms of reassuring words and praising words, the word frequency of junior middle school parents was higher than that of pre-school parents. In the category of believing words, parents whose children are in junior high school had the highest word frequency. Pre-school parents had the highest word frequency, and primary school parents had the second higher word frequency in terms of affectionate words. With regard to the missing words and panic words, junior high school parents’ word frequency was significantly higher than primary school parents, with more panic words being expressed by junior high school parents than preschool parents as well. [Limitations] This study collected the data based on the Tianya community, in which this study might ignore the possibility that some parents may still record their lives in the same post while their children’s educational stages have changed. Future research can focus more on possible influencing factors (e.g., high school parents, different roles of parents, longitudinal study) in the relationship between children’s educational stages and parents’ life satisfaction. [Conclusions] In terms of life satisfaction, junior high school parents were significantly lower than preschool and primary school parents. In terms of emotional expression, there are variations between parents whose children are at different educational stages on various emotional words, including happy, reassuring, praising, believing, affectionate, missing, panic words. " " "
Peer Review Status:Awaiting Review
Subjects: Psychology >> Applied Psychology Subjects: Computer Science >> Computer Application Technology submitted time 2021-07-08
Abstract： [Objective] By utilizing players’ behavioral data in DOTA2, this study proposed a non-intrusive method to identify the Dark Personality of game players. [Methods] After extracting behavioral features from DOTA2 replay files with the help of the parsing tool Clarity 2 package, and obtaining players’ dark personality through Dirty Dozen, we recruited machine learning methods to predict players’ sub-dimensions of Dark Personality. [Results] Results showed that best performance occurred with Gaussian Process Regression on Machiavellianism, narcissism and psychopathy. The correlations between predicting values and actual values were between 0.31 and 0.45, and the test-retest correlations were between 0.33 and 0.53. [Limitations] This study did not involve players’ verbal behavior in the process of establishing models, resulting that the features sets were not comprehensive enough. [Conclusions] It suggested that in-game behavior data was able to help predict Dark Personality of players, and the models built by Gaussian Process Regression had the best results in terms of validity and reliability.
Peer Review Status:Awaiting Review
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