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Related Concept Videos

Traits, Mood, and Subjective Wellbeing01:22

Traits, Mood, and Subjective Wellbeing

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Subjective well-being (SWB) refers to an individual's self-evaluation of their overall life satisfaction, happiness, and fulfillment. This multifaceted construct is typically assessed by analyzing the balance of positive and negative emotions alongside perceptions of life satisfaction. Personality traits such as neuroticism and extraversion are strongly associated with variations in SWB, offering critical insights into the underlying mechanisms of emotional well-being.
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Social Relationships and Well-Being01:30

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The significance of social relationships in psychological well-being is a well-established area of inquiry within social psychology. Research consistently demonstrates that the presence of meaningful, supportive relationships enhances emotional health, while the absence or deterioration of such connections can contribute to psychological distress. Relationships serve as a foundation for emotional support, identity, and social belonging, all of which are critical to an individual’s overall...
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Self-Esteem and Culture

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Self-esteem, a core psychological construct, is intricately shaped by cultural context and varies significantly between collectivist and individualistic societies. In collectivist cultures such as Japan, self-esteem tends to be flexible, context-sensitive, and influenced by relationships. A Japanese student, for instance, may show restraint in formal settings like school but behave more openly among close friends, reflecting the flexible and dynamic nature of self-concept in such...
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Central Tendency: Analysis01:10

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Measures of central tendency are tools used in biostatistics to identify the average or center of a dataset. They offer a single representative value for understanding and summarizing data distribution.
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General State of Stress01:21

General State of Stress

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The general state of stress within a material can be accurately depicted using a stress tensor. This tensor encapsulates the internal forces distributed within a material subjected to external forces or deformations.
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Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

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Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
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Prediction of Prefecture-Level Subjective Well-Being in Japan by Using Google Trends and Socioeconomic Data: Machine

Kenichi Kishi1, Hisashi Hayashi1, Shigeomi Koshimizu1

  • 1Advanced Institute of Industrial Technology, Tokyo, Japan.

JMIR Formative Research
|March 20, 2026
PubMed
Summary

Predicting Japanese subjective well-being for 2025 improved by integrating Google Trends data into stacked-ensemble models. This enhanced prediction accuracy, reducing errors in forecasting future well-being across Japan.

Keywords:
Google TrendsJapandigital traceforecastinginfodemiologyinfoveillancemachine learningsocioeconomic factorsstacked ensemblesubjective well-being

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Area of Science:

  • Computational social science
  • Psychological science
  • Data science

Background:

  • Accurate prediction of subjective well-being is crucial for policy development.
  • Traditional forecasting models may not capture real-time societal shifts.
  • Digital data sources offer potential for enhanced predictive power.

Purpose of the Study:

  • To improve the accuracy of 2025 subjective well-being predictions in Japan.
  • To evaluate the utility of Google Trends data in predictive modeling.
  • To assess the performance of leakage-controlled stacked-ensemble models.

Main Methods:

  • Utilized 2022-2025 data from Japan's 47 prefectures.
  • Developed leakage-controlled stacked-ensemble models.
  • Incorporated prespecified Google Trends indicators as features.

Main Results:

  • The model incorporating Google Trends data demonstrated improved predictive performance.
  • Mean Squared Error (MSE) for the 2025 holdout prediction was reduced from 0.0050 to 0.0045.
  • The enhancement signifies a measurable increase in prediction accuracy.

Conclusions:

  • Prespecified Google Trends indicators can significantly enhance the accuracy of subjective well-being predictions.
  • Leakage-controlled stacked-ensemble models are effective for integrating diverse data sources.
  • This approach offers a promising method for real-time monitoring and forecasting of societal well-being.