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 submitted time 2024-04-10
Abstract： Integrative complexity is a concept used in psychology to measure the structure of an individual’s thinking. It mainly involves two aspects: differentiation and integration. Differentiation refers to the ability of an individual to identify and understand different viewpoints or elements in the information. Integration refers to the ability of individuals to combine these different ideas or elements into a logical and coherent whole. The measurement of integrative complexity relies primarily on manual analysis of textual content, which can be written materials, speeches, interview transcripts, or any other form of oral or written expression. Integrative complexity has demonstrated its interdisciplinary value and extensive research potential in the fields of management psychology, political psychology and cultural psychology. In the field of management psychology, the level of integrated complexity of leaders affects how they approach complex management challenges, develop strategies, and promote team diversity. In political psychology, researchers use integrative complexity to analyze the thinking styles of political leaders, the foreign policy decision-making process, and the political attitudes and behaviors of the masses. Cultural psychology uses integrative complexity to explore the thinking patterns and information processing strategies of individuals in different cultural contexts. But in the field of health psychology, the integrative complexity has not been fully studied. Integrated complexity, as a measure of the structure of thought, can explain how individuals process information and deal with stress and negative emotions, which is very important for individual mental health. According to the suicide escape theory, individuals may escape unbearable self-consciousness and emotional pain through suicidal behavior. Under this theoretical framework, low integration complexity may be a risk factor for suicidal behavior, because low integration complexity may make it difficult for individuals to see multiple aspects of problems and possible solutions while facing stress and psychological pain, and thus leading to helpless and hopeless. This study explores the effect of integration complexity on suicidal ideation and suicidal behavior through social network media data. The results show that the complexity of dialectical integration negatively affects individual suicidal ideation, the complexity of fine integration positively affects individual suicidal ideation, and the complexity of dialectical integration negatively regulates the impact of negative emotions on suicidal ideation. Individuals with low dialectical integration complexity are more likely to be disturbed by negative emotions, and thus more likely to show suicidal ideation; Individuals with high dialectical integration complexity are less likely to be disturbed by negative emotions and thus less likely to exhibit suicidal ideation, but this pattern is not stable and may be disturbed by cultural background and other factors. On the eve of suicidal behavior, the integration complexity of the individual will continue to decrease.
Peer Review Status:Awaiting Review
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 submitted time 2024-03-18
Abstract： To assist young people infected with COVID-19 restore and develop a balanced state of mental health after the pandemic, we have developed an online self-help psychological intervention robot that can complement existing mental health resources. First, we utilized prompting engineering techniques to build a chatbot skilled in Problem-Solving Therapy (PST) based on the large language model GPT-4. Then, we conducted pre-testing and formal experiments to verify the effectiveness of the chatbot. The results of the pre-testing indicated that the chatbot followed the core work steps of PST during interactions with users. The results of the formal experiment showed that the PST chatbot performed better than the ordinary chatbot in terms of problem identification and problem-solving dimensions, indicating that the PST chatbot can help users quickly locate the problems that trouble them and develop feasible problem-solving plans. However, there was no difference between the PST chatbot and the ordinary chatbot in terms of relationship quality, and no differences were found in the evaluation of the two chatbots based on gender and post-COVID symptoms. This suggests that the PST chatbot did not significantly improve the quality of human-machine relationships, but the general acceptability and wide applicability of chatbots still have certain advantages in practical applications. The research results support the possibility of using large language models in innovative implementations of psychological self-help interventions.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Applied Psychology Subjects: Medicine, Pharmacy >> Preventive Medicine and Hygienics submitted time 2024-03-17
Abstract： HPV vaccination is an effective way to prevent and treat cervical cancer, but the vaccination situation in our country is not optimistic, and many young people hesitate to vaccinate HPV vaccine. Research has shown that information persuasion is an effective means to increase vaccination rates. This study will focus on the content of persuasion information and explore the relationship between influencing factors and individual personality characteristics. To this end, we recruited 284 subjects online to conduct a questionnaire survey and analyzed the data using ANOVA. The results show that there are significant differences in the persuasive effect of information containing different influencing factors. It is necessary to select more effective influencing factors to produce the persuasive effect of promoting vaccination, and the big five personality characteristics of individuals will have a significant impact on the persuasive effect of information. This study can provide scientific basis and guidance for the promotion of vaccination, and has important theoretical and practical value for promoting public health.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Applied Psychology submitted time 2024-02-29
Abstract： HPV vaccination can not only effectively prevent the development of cervical cancer and its precancerous lesions, but also prevent other parts of the disease caused by HPV infection. However, the vaccination situation in China is not optimistic, and many young people are hesitant to get the HPV vaccine. Based on the planning theory model, this study aims to explore the influencing factors of HPV vaccination intention, compile a questionnaire with good reliability and validity to evaluate the importance of influencing factors of HPV vaccination intention, and explore the importance degree of influencing factors of different vaccination intention. In experiment 1, this study explored the influencing factors of individual HPV vaccination intention through semi-structured interview method, and obtained 25 influencing factors such as vaccine safety, vaccine effectiveness, vaccination convenience, professionalism, conformity and data. In experiment 2, through exploratory factor analysis, confirmatory factor analysis and reliability and validity test, a 17-question, 4-dimensional questionnaire was constructed to evaluate the importance of factors influencing HPV vaccination intention. Among them, confirmatory factor analysis supported the 4-factor model (χ²/df<3, RMR=0.059, RMSEA=0.054, GFI=0.928, TLI=0.914, IFI=0.929), showing good model fit. The Cronbach’s α coefficient of the questionnaire was 0.853, and the retest reliability at a 4-week interval was 0.804. It shows that our questionnaire has good reliability and validity. In addition, there are significant differences in the evaluation of the importance of different influencing factors, and there are also significant differences in the evaluation of the importance of factors among individuals with or without a family history of cancer and different levels of education. This study will provide valuable insights into vaccination promotion strategies and provide scientific basis and reference for developing targeted approaches.
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 >> Social Psychology submitted time 2023-11-17
Abstract： The clan is a social organization entity based on blood ties, emphasizing the internal rule of the family. Clan culture is based on blood ties and values the maintenance of relationships among clan members. Being of the same clan and family is the key to identity. Clan concepts include bloodline identity, mutual assistance, filial piety, inheritance, rule of etiquette, and local customs, reflecting the inner beliefs and attitudes of clan members towards the importance and role of the family. Clan concepts and clan culture are interdependent and work together, influencing people's behavior and values, and have a profound impact on the development of modern society.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Applied Psychology submitted time 2023-07-09
Abstract： [Objective] To explore the differences of sleep quality and life satisfaction in different regions by means of microblog data, and to analyze the relationship between sleep quality and life satisfaction.
[Method] Weibo users were divided into four regions according to their posts, and the differences of sleep quality and life satisfaction were compared. [Results] (1) The sleep quality of Weibo users was marginal significant among the four regions, F (3, 489) = 2.363, p = 0.071. The sleep quality score of Weibo users in central region was significantly higher than that of Weibo users in western region (p < 0.05), but there was no significant difference in sleep quality between other regions (ps > 0.05). (2) There was no significant difference in the life satisfaction of Weibo users among the four regions, F (3, 489) = 1.490, p > 0.05. (3) Sleep quality was negatively correlated with life satisfaction (r = −0.08, p < 0.05); (4) Sleep quality did not significantly predict life satisfaction (B = −1.27, p = 0.078). [Conclusion] Sleep quality varies among users in different regions, and lower sleep quality will affect life satisfaction.
Peer Review Status:Awaiting Review
Subjects: Other Disciplines >> Synthetic discipline submitted time 2023-03-28 Cooperative journals: 《中国科学院院刊》
Abstract： Wuhan lockdown has affected individual psychological status while effectively curbing the epidemic. It is difficult for traditional questionnaire method to acquire individual psychological assessments in large scale timely, which makes it hard to propose time-effective psychological services. In this study, we extracted the linguistic features of Weibo posts from January 20, 2020 to February 16, 2020 among 41 105 active users, and identified the changes of Wuhan citizens’ psychological status nonintrusively. The results indicated that Wuhan citizens have been in a high state of negative emotions such as anxiety, anger, hostility, and disappointment in short term. Meanwhile, the lockdown had induced more fear, psychological pain, and stress experience to some extent. In addition, the lockdown reduced the usage of leisure words and increased the frequency of working words in Wuhan residents’ language expressions. The results suggest that we should take targeted services according to the different negative emotions raised, and arrange long-term service for negative effects of stress, and incorporate online public psychological detection and service into national emergency management system.
Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： Modeling psychological indexes (i.e., psych-modeling) is an emerging method that uses machine learning algorithms to identify psychological indexes based on big data. This paper reviews the feasibility of psych-modeling methods based on social media data in the field of psychometrics. Frequently used data types and machine learning algorithms are introduced. Then, we summarize psych-modeling's application to various scenarios together with its strengths and weaknesses. Compared with traditional self-reporting methods, psych-modeling has some advantages, including better performance in retrospective studies, greater ecological validity, and greater time-efficiency. However, psych-modeling has several limitations. For example, researchers need to spend extra time and effort to learn this new method and bear the inevitable cost of hardware. In future studies, researchers could investigate further how user's behavior on social media relates to psychological indexes. We also expect psych-modeling will be used in future psychological studies. By combining psychometrics and machine learning, we believe psych-modeling could make great contributions to psychology research and practice in the future.
Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： With the further development of computers and big data technology, human society and its cultural forms are undergoing profound changes. The production and interaction of cultural symbols have become increasingly complex, and cultural members and their social networks have left numerous texts and behavior footprints, which makes it necessary to describe, predict, and even change the culture, so that computable cultural symbols and their interaction process have gradually become the research object of cultural psychology. In this vein, Computational Cultural Psychology (CCP), which employs big data and computation tools to understand cultural symbols and their interaction processes, has emerges rapidly, making large-scale or even full sample cultural analysis possible. The key variables of CCP are mainly about individualism and collectivism, and the analysis technologies include feature dictionaries, machine learning, social networks analysis, and simulation.New research avenues of CCP involve the cultural change effect from the temporal perspective and cultural geography effect from the spatial perspective. For the former, Google Ngram Viewer, Google News, Google Search, name archives, pop songs, and micro-blogs were used to analyze the cultural changes after the long-term historical development and the short-term economic transformation. For the latter, both social media (e.g., Twitter, Facebook, and Weibo) and large-scale survey were used to analyze the cultural differences of various countries or regions in different geographic spaces, as well as the relationship between culture and environment, such as cultural diversity along the "Belt and Road", person - environment fit and cultural value mismatch across different regions in a country or all over the world.It should be noted that there are several limitations in CCP, including decoding distortion, sample bias, semasiological variation, and privacy risk, although new methods and paradigms are provided. In future directions, theoretical interpretation of variables, cultural dynamics, interdisciplinary integration, and ecological validity should be seriously concerned. In particular, accurate definition and theoretical interpretation of big data measurement are needed; a variety of big data corpus (e.g., historical archives) should be used for the evolutionary analysis of dynamic cultures; deep integration, but not conflict, should be encouraged between culture psychology and the sciences of computer, communication, and history; and the "scenarios" of big data should be considered in promoting the ecological validity of cultural psychology.Taken together, a review of the emergence of CCP, as well as the empirical research on the big data analysis of cultural change and cultural geography, is helpful in understanding the advantages, limitations, and future direction of this new field, which sheds light on theoretical and methodological innovation of cultural psychology.
Subjects: Psychology >> Social Psychology Subjects: Psychology >> Industrial Psychology submitted time 2022-11-07
Abstract： With the integrated development of big data technology and cultural psychology, computational cultural psychology came into being as a novel interdisciplinary research field, which makes large-scale cultural analysis possible. The key variables of computational cultural psychology are mainly about individualism and collectivism, and the big data technologies (e.g., feature dictionaries, machine learning, social networks analysis, and simulation) have been used to analyze the cultural change effect from the temporal perspective and cultural geography effect from the spatial perspective. It should be noted that there are several limitations in Computational Cultural Psychology, including decoding distortion, sample bias, semasiological variation, and privacy risk, although new method and paradigm are provided. In future directions, theoretical interpretation of variables, cultural dynamics, interdisciplinary integration, and ecological validity should be seriously concerned.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Applied Psychology submitted time 2022-10-18
Abstract： The campus life of college students has undergone a tremendous change since the outbreak of COVID-19. Due to the constant adjustment of the policy, the closure of schools and dormitories, students’ communication and leisure activities are restricted, so their mood fluctuates, which cannot be fully reflected by traditional scales. College students live a group life, their mentality has social and group attributes, whose formation and change involve many factors and is not simply the accumulation and mechanical superposition of individual mindsets. The Population Mental Health Measurement (DASS) tends to be the measurement when negative emotions are severe, so it cannot fully express the “positive” attitude when the epidemic is getting better.SCL-90 (90 Symptom Checklist) and SDS (Depression Status Scale) usually tend to measure individuals rather than studies on risk perception and psychological behavior. After research, the mental awareness scale during the COVID-19 was prepared and tested. Our research has positive implications for the public’s mental perception under COVID-19. Only by understanding the changes in the mindset of the public can we carry out targeted psychological counseling and win the people’s war against the epidemic.
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：
In order to explore the differences in life satisfaction and emotional word frequency between single people and married people, this study uses Python to collect data from Zhihu, China’s largest Q&A platform, and obtain the corresponding group's sentiment frequency ratio through the "Wenxin" system and life satisfaction score by a life satisfaction prediction model. Results showed that the life satisfaction score in the married group was significantly higher than that in the single group (t=4.415, p<0.001); The proportion of positive emotion words (t=-9.061, p<0.001) and anxiety words (t=1.844, p<0.001) in the married group was significantly lower than that in the single group, but the proportion of anger words (t=5.101, p<0.001) was significantly higher than that in the single group. The results show that while married people obtain higher life satisfaction and lower anxiety level, they also need to deal with partner related emotional problems."
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 submitted time 2022-03-06
Abstract：
[Objective] This study aims to explore the relationship between staying up late, different development levels of cities and life satisfaction with the method of big data in Weibo, so as to increase the understanding of life satisfaction of contemporary people. [Method]The users in Weibo were divided into those who stayed up late and didn't stay up late in first-tier cities and other cities according to user’s information of blog posting. In addition, the statistical difference in life satisfaction between people who stayed up late and didn't stay up late in different areas was compared. [Results] (1) The life satisfaction of Weibo users who stayed up late was significantly higher than that of non-staying up late group (t = 11.768, p < 0.05); (2) Life satisfaction of Weibo users in first-tier cities was significantly lower than that of users in other cities (t =-4.135, p < 0.05); (3) The life satisfaction of staying up late in first-tier cities was significantly lower than that of staying up late in other cities (p < 0.05), and there was no statistically significant difference between non-staying up late in first-tier cities and non-staying up late in other cities (p > 0.05); (4) The life satisfaction of staying up late in first-tier cities was significantly higher than that of non-staying up late in first-tier cities (p < 0.05), and that of staying up late in other cities was significantly higher than that of non-staying up late (p < 0.05). [Conclusion] The staying up late behavior of contemporary Weibo users will improve their life satisfaction to a certain extent. However, the life satisfaction of Weibo users in first-tier cities is lower than that of Weibo users in other cities. "
Peer Review Status:Awaiting Review
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