{"id":46784,"date":"2023-11-30T00:00:00","date_gmt":"2023-11-30T08:00:00","guid":{"rendered":"https:\/\/griddb-linux-hte8hndjf8cka8ht.westus-01.azurewebsites.net\/blog\/analyzing-social-medias-correlation-with-mental-health-using-griddb\/"},"modified":"2025-11-13T12:56:49","modified_gmt":"2025-11-13T20:56:49","slug":"analyzing-social-medias-correlation-with-mental-health-using-griddb","status":"publish","type":"post","link":"https:\/\/www.griddb.net\/en\/blog\/analyzing-social-medias-correlation-with-mental-health-using-griddb\/","title":{"rendered":"Analyzing Social Media’s Correlation with Mental Health using GridDB"},"content":{"rendered":"
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With the information getting polarized on social media, it is natural to think if it affects our mental health in real life as well. In this tutorial, we explore the correlation of mental health with social media usage using Python and GridDB. The outline of the tutorial will be as follows:<\/p>\n

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  1. Prerequisites and Environment Set up<\/li>\n
  2. About the Dataset<\/li>\n
  3. Importing the libraries<\/li>\n
  4. Reading the data<\/li>\n
  5. Exploratory Data Analysis<\/li>\n
  6. Data Transformation<\/li>\n
  7. What is GridDB?<\/li>\n
  8. Inserting and Reading data from GridDB<\/li>\n
  9. Basic Data Visualization<\/li>\n
  10. Quantifying the mental health<\/li>\n
  11. Correlation between mental health and social media usage<\/li>\n
  12. Conclusion<\/li>\n
  13. References<\/li>\n<\/ol>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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    1. Prerequisites and Environment Set up\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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    1.1 Python Libraries\u00b6<\/a><\/h3>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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    This tutorial has been executed in Jupyter Notebook with Python version 3.8.3 on a Windows 10 Operating System. The following libraries will be required before the code execution:<\/p>\n

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    1. Pandas<\/a><\/li>\n
    2. Numpy<\/a><\/li>\n
    3. Matplotlib<\/a><\/li>\n
    4. Seaborn<\/a><\/li>\n<\/ol>\n

      The easiest way to install these packages in your environment is via pip install <package-name><\/code> or conda install <package-name><\/code> if you are using Anaconda Navigator. Alternatively, refer to the documentation of these packages for detailed information<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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      1.2 GridDB Libraries\u00b6<\/a><\/h3>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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      Additionally, if you want to read your data from GridDB to directly in Python. The following libraries need to be installed:<\/p>\n

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      1. GridDB C-client<\/a><\/li>\n
      2. SWIG (Simplified Wrapper and Interface Generator)<\/li>\n
      3. GridDB Python Client<\/a><\/li>\n<\/ol>\n

        GridDB python-client makes it easier to load the data into your python environment without any hassle of downloading it first into a CSV format.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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        Great! Now that we have installed the necessary libraries, let's have a look at the dataset.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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        2. About the Dataset\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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        The dataset used for this tutorial intends to find a correlation (if any) between the time spent on social media by an individual and how their mental health gets affected by it. The dataset is open-source and can be downloaded from Kaggle<\/a>.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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        The dataset has different columns recording an individual's personal information such as age, gender, occupation, along with questions about social media usage, mental health status, etc. In total, there are 481 instances (or rows) and 20 such attributes (or columns).<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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        Let's import the libraries and have a look at our dataset<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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        3. Importing the libraries\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
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        In\u00a0[64]:<\/div>\n
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        <\/span>import<\/span> <\/span>pandas<\/span> <\/span>as<\/span> <\/span>pd<\/span>\nimport<\/span> <\/span>numpy<\/span> <\/span>as<\/span> <\/span>np<\/span>\nimport<\/span> <\/span>matplotlib.pyplot<\/span> <\/span>as<\/span> <\/span>plt<\/span>\nimport<\/span> <\/span>seaborn<\/span> <\/span>as<\/span> <\/span>sns<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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        The cell will not return anything if the execution is successful. If you encounter an error, kindly check if the packages are correctly installed and are compatible with each other.<\/p>\n
        After importing the libraries, let's go ahead and load our dataset<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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        4. Reading the data\u00b6<\/a><\/h2>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
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        In\u00a0[65]:<\/div>\n
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        <\/span>data<\/span>=<\/span>pd<\/span>.<\/span>read_csv<\/span>(<\/span>'social_media.csv'<\/span>)<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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        Now that our data is successfully loaded in the environment, let's have a look at some basic features. We'll begin with the size of the dataset and how it looks!<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
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        In\u00a0[66]:<\/div>\n
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        <\/span>data<\/span>.<\/span>shape<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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        Out[66]:<\/div>\n
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        (481, 21)<\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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        As mentioned before, there are 481 rows and 21 columns. Let's see what that looks like.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>
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        In\u00a0[67]:<\/div>\n
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        <\/span>data<\/span>.<\/span>head<\/span>()<\/span>\n<\/pre><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
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        Out[67]:<\/div>\n
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