Subjects: Psychology >> Applied Psychology Subjects: Computer Science >> Computer Application Technology submitted time 2024-04-10
Abstract： Integrative complexity is a concept used in psychology to measure the structure of an individual’s thinking in two aspects: differentiation and integration. The measurement of integrative complexity relies primarily on manual analysis of textual content, which can be written materials, speeches, interview transcript large language models, or any other form of oral or written expression. To solve the problems of high cost of manual assessment methods, low accuracy of automated assessment methods, and the lack of Chinese text assessment scheme, this study designed an automated assessment scheme for integrative complexity on Chinese and English texts. We utilized text data enhancement technique of the large language model and the model migration technique for the assessment of integrative complexity, and explored the automated assessment methods for the two sub-structures of integrative complexity, namely, the fine integration complexity and the dialectical integration complexity. In this paper, two studies are designed and implemented. Firstly, a prediction model for the integration complexity of English text is implemented based on the text data enhancement technology of large language model; secondly, a prediction model for the integration complexity of Chinese text is implemented based on the model transfer technology. The results showed that: 1) We used GPT-3.5-Tubo for English text data enhancement, a pre-trained multilingual Roberta model for word vector extraction, and a text convolutional neural network model as a downstream model. The Spearman correlation coefficient between this model’s prediction of integration complexity and the manual scoring results was 0.62, with a dialectical integration complexity correlation coefficient of 0.51 and a fine integration complexity Spearman correlation coefficient of 0.60. It is superior to machine learning methods and neural network models without data enhancement. 2) In Study 2, a model with the same structure as the neural network in Study 1 was established, and the final model parameters in Study 1 were also transferred to the model in this study to train the integration complexity prediction model based on Chinese text. In the case of zero samples, the Spearman correlation coefficients of the transfer learning model for integrative complexity are 0.31, the Spearman correlation coefficient of dialectical integration complexity is 0.31, and the correlation coefficient of fine integration complexity is 0.33, all of which are better than the model in the case of random parameters (integrative complexity: 0.17, dialectical integrative complexity: 0.10, fine integrative complexity: 0.10). In the case of small samples, the Spearman correlation coefficient of the transfer learning model was 0.73, with a dialectical integration complexity correlation coefficient of 0.51 and a fine integration complexity correlation coefficient of 0.73.
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Subjects: Psychology >> Applied Psychology Subjects: Computer Science >> Computer Application Technology submitted time 2024-04-09
Abstract： In recent years, the Linguistic Inquiry and Word Count (LIWC) tool has garnered increasing attention, offering the promise of objective, automated, and transparent psychological text analysis. This resurgence has reignited enthusiasm among psychologists for language analysis research. The recent revision of the LIWC-22 dictionary has introduced numerous variables aimed at assessing various socio-psychological structures, thus expanding the application potential of the LIWC tool. To further promote the cultural adaptation of the LIWC tool, we have revised and validated the Simplified Chinese Linguistic Inquiry and Word Count Dictionary 2024 (SCLIWC2024) to better align with the features of LIWC-22. In Study One, building upon the SCLIWC dictionary, we revised SCLIWC2024 by comparing it with the LIWC-22 and CLIWC2015 dictionaries. In Study Two, we conducted two experiments to validate the efficacy of SCLIWC2024 in detecting different psychological semantics in online texts, addressing crucial questions regarding how to more effectively utilize SCLIWC2024 for detecting the psychological semantics of short texts on social networking platforms.
Peer Review Status:Awaiting Review
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.
Peer Review Status:Awaiting Review
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 Subjects: Computer Science >> Computer Application Technology submitted time 2024-02-29
Abstract： Objective With the development of network big data and machine learning, more and more studies starting to combine text analysis and machine learning algorithms to predict individual satisfaction. In the studies focused on building life satisfaction prediction models, it is often difficult to obtain large amounts of valid and labeled data. This study aims at solving this problem using data augmentation and optimizing the prediction model of life satisfaction. Method Using 357 life status descriptions annotated by self-rating life satisfaction scale scores as original text data. After preprocessing using DLUT-Emotionontology, EAD and back-translation method was applied and the prediction model was built using traditional machine learning algorithms. Results Results showed that (1) the prediction accuracy was largely enhanced after using the adapted version of DLUT-Emotionontology; (2) only linear regression model was enhanced after data augmentation; (3) rigid regression model showed the greatest prediction accuracy when trained by original data (r = 0.4131). Conclusion The improvement of feature extraction accuracy can optimize the current life satisfaction prediction model, but the text data augmentation methods, such as back translation and EDA may not be applicable for the life satisfaction prediction model based on word frequency.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Cognitive Psychology Subjects: Computer Science >> Computer Application Technology submitted time 2022-03-21
Abstract：
[Objective] The dream world, as a virtual reality world generated by brain, is resemble with the metaverse. Different psychological hypotheses have explained why the brain could constructs the immersive, realistic virtual world in dreams.
[Methods] This paper reviews the theoretical hypotheses about dreams that the application of metaverse can refer and help to to enhance people’s self-worth and happiness.
[Results] The design of future metaverse can refer to the innate virtual reality experience of dream, and play a role to facilitate human behavior and mental health.
[Limitations] This review has not systematically reviewed the behavioral and neural mechanisms of dreams because of the limited papers of dream studies.
[Conclusions] Based on review of theories, functions and the mechanism of dreams, theoretical guidance, suggestions and challenges for the development of metaverse are provided and discussed in this paper.
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
Subjects: Psychology >> Applied Psychology Subjects: Computer Science >> Computer Application Technology submitted time 2020-03-08
Abstract： Personality affects the individual's work and life style, and has important guiding significance for the individual's psychological counseling and career development. Traditional methods use personality scales to evaluate personality scores, which include problems such as individual refusal to answer and blind answering. In recent years, with the development of machine learning, new ideas have been provided for personality recognition. This article uses participants' self-introduction videos and Big Five personality scale scores to obtain different prediction models for different personality dimensions through key point extraction, feature dimension reduction, modeling, and iterative tuning. This article uses participants' self-introduction videos and Big Five personality scale scores to obtain different prediction models for different personality dimensions through key point extraction, feature dimension reduction, modeling, and iterative tuning. The test results show that the personality prediction model based on the self-introduction video is close to or achieves medium correlation in all dimensions, and can provide non-intrusive automatic personality recognition,, which provides new ideas for personality measurement.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Applied Psychology Subjects: Computer Science >> Computer Application Technology submitted time 2020-01-07
Abstract： "
Peer Review Status:Awaiting Review
Subjects: Psychology >> Applied Psychology Subjects: Computer Science >> Computer Application Technology submitted time 2019-12-20
Abstract： [Background] Based on counting frequency of specially selected words, LIWC (known as Linguistic Inquiry and Word Count) is a useful tool to analyze expressions of writings or other texts created by individuals or group, for purpose of figuring out the psychological meanings inside the texts. In ancient China, the classical style of writing has a striking difference with modern times. In order to analyze the psychological meanings of classical Chinese text, we construct a Classical Chinese version of LIWC dictionary (known as CC-LIWC), based on the 2015 edition of Simplified Chinese LIWC (known as SC-LIWC). [Objective] In this paper, we show the constructing process of CC-LIWC and give an example of how to use the dictionary to analyze classical Chinese text. [Methods] First, we obtain all the words (including modern Chinese and Classical Chinese words) and their corresponding explanations from the online Chinese dictionary and keep the classical Chinese words with their modern translation; second, we search SC-LIWC words in the explanations. In this way, SC-LIWC words are mapping with the classical Chinese words; finally, we invite ancient Chinese based professionals to check the mapping results manually to ensure the consistency and accuracy of the results. [Results] The final dictionary includes 81 categories and 49136 classical Chinese entries. [Limitations] In classical Chinese context, polysemy or diversity of a word is very common, which affects the classification of words in the dictionary. [Conclusion] we use CC-LIWC to analyze The Analects(excerpts) and The Isolated Indignation. The result shows the difference between the moderation of Confucian and the dialectical thinking of Legalist. Therefore, CC-LIWC dictionary can distinguish the expression tendency of text efficiently.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Social Psychology Subjects: Computer Science >> Computer Application Technology submitted time 2018-05-21
Abstract：
[Background] The adverse effects of childhood sexual abuse experience on female physical and psychological health are enduring. However, few studies have focused on the psychological characteristics of this group when they grew up.
[Objective] The purpose of this study was to explore the difference in psychological characteristics including social attitudes, well-being, and mental health between the females with sexual abuse experience in childhood (CSA group) and females without this experience (control group) based on the microblogging data calculation model.
[Methods] This study collected 46 victims (all females) and 46 non-victims (sex matching with CSA group) on Sina Weibo, crawled all the microblogs of the selected users and and calculate its score on various psychological characteristics by microblogging data calculation model.
[Results] Using independent sample t-test, the results showed that there were significant differences in social attitudes, well-being, and especially mental health. At the same time, we also found that there were differences in microblog behavior characteristics between the two groups. Compared with non-victims, the victims had higher scores in depression, stress and other health characteristics, and lower scores in psychological characteristics such as life satisfaction and self-acceptance. However, they did not reach the critical thresholds for the diagnosis of mental diseases.
[Limitations] The psychological features obtained from microblogging data calculation model can not completed equivalent to the psychological features obtained from psychological scales and can not replace the rigid psychological measurement.
[Conclusion] The childhood sexual abuse experience has negative effect on female. However, this effect is not sufficient to meet the threshold criteria for mental illness.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Applied Psychology Subjects: Computer Science >> Computer Application Technology submitted time 2018-05-09
Abstract： By using social media big data, this study explores the role of regional factors in the process of haze influence. Based on keyword frequency, we analyzed weibo original content of users from Beijing(Chaoyang district) and Chengdu, after delete hot events which greatly influence people’s emotion. We introduced of regulating variable (area) and found that there is interaction of areas and haze(PM2.5), haze positively related with negative emotion in Beijing and haze negatively related with negative emotion in Chengdu. There are regional differences in the influence of haze (PM2.5), which can be related to the lifestyle and historical culture of the two cities.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Social Psychology Subjects: Computer Science >> Computer Application Technology submitted time 2018-03-15
Abstract： 理清“一带一路”沿线国家或地区的民心特点，并找到有效的合作交往模式，是关系到国家战略实施的重大问题。但是，由于地域辽阔、民族众多，且地缘政治、经济、文化因素（如原苏联影响、欧美国家殖民、宗教传统等）异常复杂，传统的分析方法往往难以奏效。该研究结合文化心理学和大数据分析技术，利用社交媒体Twitter数据来分析“一带一路”沿线国家或地区的自我表征特点（独立性或个人主义），并建立自我表征与社会信任（普遍信任、特殊信任）的预测模型，以探究与“一带一路”沿线国家或地区合作交往的行为模式，即：自我表征是独立，还是互依；人际关系偏好是陌生人之间的普遍信任，还是熟人间的特殊信任。结果表明，“一带一路”沿线国家或地区在自我独立性这一个人主义文化指标上存在较大的变异，且主要受欧美国家殖民历史和当地宗教传统的影响；此外，针对陌生人、外国人的普遍信任与针对家人、熟人的特殊信任，可以通过个人主义指标来预测。总之，“一带一路”沿线的文化是多样的，可以通过社交媒体产生的海量语料库快速计算其个人主义指标，并以此来建立自我表征与社会信任的预测模型。该研究为分析“一带一路”战略区域的“民心”特点、探索当地合作交往的行为模式提供了新的技术路径。
Peer Review Status:Awaiting Review
Subjects: Psychology >> Applied Psychology Subjects: Computer Science >> Computer Application Technology submitted time 2018-03-05
Abstract：[目的] 利用微博大数据探索熬夜和焦虑、抑郁情绪的关系。 [方法] 本研究根据微博用户在夜间的活动状态, 把100万活跃用户分为熬夜组和非熬夜组，比较两组用户在所发微博中出现的体现焦虑和抑郁情绪的相关词词频。 [结果] 独立样本t检验结果显示，熬夜组的焦虑相关词词频显著高于非熬夜组，t=36.86，p<0.001；熬夜组的抑郁相关词词频显著高于非熬夜组，t=49.71，p<0.001。 [局限] 词频分析与用心理测量量表测量抑郁和焦虑的情感无法完全等同，基于大数据的词频分析虽然提供了一种高效的分析方法，但不能完全替代严格的心理测量。 [结论] 入睡时间过晚会影响睡眠质量；熬夜人群更容易受到焦虑和抑郁情绪的困扰。
Peer Review Status:Awaiting Review
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