Table of Links Abstract and Introduction
Methodology
Results
Discussion and Reference Abstract and Introduction Abstract and Introduction Methodology Methodology Results Results Discussion and Reference Discussion and Reference Methodology Search Strategy The search focused on publications on machine learning and deep learning models for detecting depression and other mental health conditions using social media data, primarily from platforms like Twitter, Facebook, and Reddit. To identify relevant studies, a systematic search was conducted across multiple academic databases including PubMed, and IEEE Xplore, with Google Scholar used for additional sources. The search terms included combinations of "machine learning", "deep learning", "artificial intelligence", "social media", "Twitter", "Facebook", "Reddit", "depression", "sentiment analysis", and "mental health". To broaden the scope of the search, additional terms such as "anxiety", "mental disorders", "neural networks", and "supervised learning" were included. The search process was carried out from June to July 2024. The search strategy was structured around three main categories: social media platforms (e.g., "social media", "Twitter", "Facebook", "Reddit"), mental health topics (e.g., "depression", "sentiment analysis"), and machine learning and data analysis techniques (e.g., "machine learning", "deep learning", "Artificial Intelligence"). The comprehensive search query formulated for this review is: (("social media" OR "Twitter" OR "Facebook" OR “Reddit”) AND ("depression" OR "sentiment analysis" OR "mental health" OR "anxiety" OR "mental disorders") AND ("machine learning" OR "deep learning" OR "artificial intelligence" OR "neural networks" OR "supervised learning")). Inclusion and Exclusion Criteria To be included in this review, studies needed to meet the following criteria: Publication Date: Studies published after 2010 were included to ensure contemporary research and methods were considered




Language: Only studies published in English were included.


Research Focus: The study must use machine learning or deep learning models for detecting depression or other mental health conditions, with a particular focus on analyzing data from social media platforms like Twitter, Facebook, or Reddit.


Study Type: The review included primary research articles, specifically those that involved data-driven analyses. Studies were excluded based on the following criteria:


Publication Type: Review articles, systematic reviews, conference abstracts, editorials, opinion pieces, and non-peer-reviewed literature were excluded.


Scope: Studies not directly focused on mental health detection through social media or those lacking the application of machine learning models were excluded.


Methodology: Studies that did not directly employ machine learning or deep learning models and instead relied solely on quantitative analysis were excluded Publication Date: Studies published after 2010 were included to ensure contemporary research and methods were considered Publication Date: Studies published after 2010 were included to ensure contemporary research and methods were considered Language: Only studies published in English were included. Language: Only studies published in English were included. Research Focus: The study must use machine learning or deep learning models for detecting depression or other mental health conditions, with a particular focus on analyzing data from social media platforms like Twitter, Facebook, or Reddit. Research Focus: The study must use machine learning or deep learning models for detecting depression or other mental health conditions, with a particular focus on analyzing data from social media platforms like Twitter, Facebook, or Reddit. Study Type: The review included primary research articles, specifically those that involved data-driven analyses. Studies were excluded based on the following criteria: Study Type: The review included primary research articles, specifically those that involved data-driven analyses. Studies were excluded based on the following criteria: Publication Type: Review articles, systematic reviews, conference abstracts, editorials, opinion pieces, and non-peer-reviewed literature were excluded. Publication Type: Review articles, systematic reviews, conference abstracts, editorials, opinion pieces, and non-peer-reviewed literature were excluded. Scope: Studies not directly focused on mental health detection through social media or those lacking the application of machine learning models were excluded. Scope: Studies not directly focused on mental health detection through social media or those lacking the application of machine learning models were excluded. Methodology: Studies that did not directly employ machine learning or deep learning models and instead relied solely on quantitative analysis were excluded Methodology: Studies that did not directly employ machine learning or deep learning models and instead relied solely on quantitative analysis were excluded Study Selection Process The selection process was conducted in three stages to ensure a rigorous and unbiased review of relevant studies: 1. Initial Identification: Duplicates were removed, and an initial screening was conducted based on titles and abstracts to filter out irrelevant studies. 2. Title and Abstract Screening: Independent review by two researchers to assess relevance based on titles and abstracts. Any discrepancies were discussed and resolved to ensure a consistent screening process. 3. Full-Text Screening: A comprehensive review of the full texts of selected studies was conducted. Any disagreements were resolved through discussion to maintain an unbiased selection process. Additionally, relevant studies identified through references in full-text articles were included for consideration. Data Extraction and Analysis The data extraction process involved using a standardized form to systematically capture detailed information from each selected study. The form included fields to record author names, study titles, publication journals, and publication years. It also documented the study designs, settings, and sample sizes, alongside specific inclusion and exclusion criteria. In addition, the form provided details on the machine learning models employed, the social media platforms analyzed (such as Twitter, Facebook, and Weibo), and the primary and secondary outcomes measured. Additionally, performance metrics, including accuracy, precision, recall, F1 score, and Area Under the Receiver Operating Characteristic Curve (AUROC), were collected when applicable. Special attention was given to identifying potential sources of bias, study limitations, and funding sources, ensuring a comprehensive overview of each study's context and reliability. Table 1 below outlines the key categories and details included in the data extraction form. This structured approach to data extraction provided a systematic and comprehensive overview of each study, ensuring that critical aspects relevant to machine learning applications in mental health detection were thoroughly documented. Analytical Methods Used to Synthesize Findings The extracted data were synthesized using a narrative approach, systematically examining each aspect of the machine learning lifecycle—sampling, data preprocessing, model construction, tuning, evaluation, comparison, and reporting—across the selected studies. This synthesis involved reviewing how studies approached sampling and data preprocessing, examining their approaches to model construction and tuning, and assessing model evaluation and comparison based on quantitative metrics such as accuracy, precision, recall, F1 scores, and AUROCs. For each stage, we summarized the methodologies employed by the studies and identified potential biases with established tools. This comprehensive approach provided insights into the current state of research, highlighting areas for future investigation to enhance the accuracy, generalizability, and applicability of machine learning models in this field. Authors:
(1) Yuchen Cao, Khoury college of computer science, Northeastern University;
(2) Jianglai Dai, Department of EECS, University of California, Berkeley;
(3) Zhongyan Wang, Center for Data Science, New York University;
(4) Yeyubei Zhang, School of Engineering and Applied Science, University of Pennsylvania;
(5) Xiaorui Shen, Khoury college of computer science, Northeastern University;
(6) Yunchong Liu, School of Engineering and Applied Science, University of Pennsylvania;
(7) Yexin Tian, Georgia Institute of Technology, College of Computing. Authors: Authors: (1) Yuchen Cao, Khoury college of computer science, Northeastern University; (2) Jianglai Dai, Department of EECS, University of California, Berkeley; (3) Zhongyan Wang, Center for Data Science, New York University; (4) Yeyubei Zhang, School of Engineering and Applied Science, University of Pennsylvania; (5) Xiaorui Shen, Khoury college of computer science, Northeastern University; (6) Yunchong Liu, School of Engineering and Applied Science, University of Pennsylvania; (7) Yexin Tian, Georgia Institute of Technology, College of Computing. This paper is available on arxiv under CC BY 4.0 license. This paper is available on arxiv under CC BY 4.0 license. available on arxiv

Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.

Facebook

AI, Depression & Social Media: What New Research Tells Us

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

A Comprehensive Overview of Image Generation Models: From GANs to Diffusion Techniques

5 Ways AI is Radically Transforming the Healthcare Industry

6 Ways Emotion AI is Changing the Healthcare Industry

7 ways AI Revolutionizes Cancer Prediction, Detection, and Treatment

9 diagnostic aid AI products in 2021

Accelerating Innovation: How Covid Has Prompted Technological Evolution Within Healthcare

A Comprehensive Overview of Image Generation Models: From GANs to Diffusion Techniques

5 Ways AI is Radically Transforming the Healthcare Industry

6 Ways Emotion AI is Changing the Healthcare Industry

7 ways AI Revolutionizes Cancer Prediction, Detection, and Treatment

9 diagnostic aid AI products in 2021

Accelerating Innovation: How Covid Has Prompted Technological Evolution Within Healthcare

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps