effects of social media on mental health

 Methods:

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The independent variables in the logistic regression model included scores reflecting different mental health symptoms collected from Likert scale responses, as well as demographic data such as age and gender. Whether or not a person was advised to obtain a mental health examination based on their overall score that indicated the intensity of their symptoms was the dependent variable, or the intended result.

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The logistic regression model uses independent variables like mental health symptom score, which were collected from a Likert scale, and demographic factors like age and gender to assess if a person should get a mental health examination or not based on the severity of their symptoms, which is the dependent variable.

Introduction:

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Our research paper utilizes the two different algorithms on the mental health dataset. Initially, we gathered age, gender, relationship status, jobs, along with the time spent on social media. We have analyzed the relationship using Logistic regression and Gaussian Naive Bayes algorithm.

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The research paper uses two models (Logistic regression and Gaussian Naive Bayes) to learn the relationship between the use of social media and mental health. Firstly, information on factors such as age, sex, job status, the user’s relationship status, and time spent on social media was gathered.

Results:

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Performance measures like recall, accuracy, precision, and F1-score were computed to assess how well logistic regression model and Gaussian Naive Bayes classifier predicted the result. All things considered, logistic regression was a useful technique for evaluating the data and forecasting the probability that people will exhibit serious mental health symptoms, which advanced knowledge in the field.

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Performance factors such as recall, accuracy, precision, and F1-score were calculated to evaluate how logistic regression and the Gaussian Naïve Bayes model predicted the result. Overall, logistic regression was a more useful model to interpret the data and forecast the chances of people who will present with serious mental health symptoms. This information helped in advancing knowledge in the field of medicine.

Discussion:

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The logistic regression has higher model complexity because of it‘s ability to capture the non-linear relationships between features and the target variable. Whereas naive bayes fails due to it’s strong feature independence assumption.

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The ability of logistic regression to interpret a non-linear relationship between features and target variables makes this model a higher complexity model. On the other hand, the Gaussian Naive Bayes model assumes that variables are independent of each other, and therefore, it is unable to capture the complex relationship between social media and mental health variables.

• What future research should follow up on this work?

Some future research ideas that should be followed up on are using more variable factors, such as the type of content consumed on social media platforms, and how it affects the mental health of users. Long-term effects of social media use on mental health can also be studied as future research.

Difficult Material:

• What did you not understand about this paper that someone else may be able to help you with? Or, if you understood everything, what did you find most challenging to understand?

I did not understand the data visualization using the pair slot shown in Fig 2. How are different variables like age, sex, time spent, ADHD, anxiety, self-esteem, and depression related to each other? What do outcomes 0 and 1 mean?