Research & Statistics
Discover cutting-edge research and publications leveraging AI and machine learning to advance mental health understanding and intervention.
Our Research
Machine Learning in Mental Health Assessment
A comprehensive study on the application of machine learning algorithms in mental health diagnosis and treatment planning.
Natural Language Processing in Mental Health
Exploring the potential of NLP in analyzing patient narratives and identifying early signs of mental health conditions.
Research Impact
Our research citations have grown significantly, reflecting its influence in the field.
Research Papers Published
Detecting Mental Disorders in Social Media through Emotional Patterns: The Case of Anorexia
Authors: Y. Nishant, Ritesh G.N., P. Indra Naik, E. Manisai, Dr. Sarlana Sandhya Rani
Publisher: International Journal of Modern Engineering Research and Technology
This study focuses on detecting mental disorders by analyzing emotional patterns in social media posts, specifically targeting anorexia.
Access Paper →Detecting Mental Disorders in Social Media Through Emotional Patterns: The Case of Anorexia and Depression
Authors: Mario Ezra Aragón, Antonio Paredes-López, Manuel Montes-y-Gómez, Hugo Jair Escalante
Publisher: IEEE Transactions on Affective Computing
This research analyzes computational representations modeling the presence and changes of emotions expressed by social media users to detect anorexia and depression.
Access Paper →Emotion-based Modeling of Mental Disorders on Social Media
Authors: Xiaobo Guo, Yaojia Sun, Soroush Vosoughi
Publisher: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
This paper proposes a model for passively detecting mental disorders by analyzing emotional states and their transitions in Reddit conversations.
Access Paper →Mental Disorders on Online Social Media Through the Lens of Language and Behaviour: Analysis and Visualisation
Authors: Esteban A. Ríssola, Mohammad Aliannejadi, Fabio Crestani
Publisher: arXiv
This study undertakes a thorough analysis to better understand the factors that characterize and differentiate social media users affected by mental disorders, focusing on language use and online behavior.
Access Paper →Detection and Classification of Mental Illnesses on Social Media Using RoBERTa
Authors: Ankit Murarka, Balaji Radhakrishnan, Sushma Ravichandran
Publisher: arXiv
This research proposes a solution to detect and classify five prominent kinds of mental illnesses by analyzing unstructured user data on social media platforms using a Transformer-based architecture.
Access Paper →Language and Mental Health: Measures of Emotion Dynamics from Text as Linguistic Biosocial Markers
Authors: Daniela Teodorescu, Tiffany Cheng, Alona Fyshe, Saif M. Mohammad
Publisher: arXiv
This work studies the relationship between tweet emotion dynamics and mental health disorders, providing evidence for how linguistic cues can serve as biosocial markers for mental illnesses.
Access Paper →Emotion Fusion for Mental Illness Detection from Social Media: A Survey
Authors: Tianlin Zhang, Kailai Yang, Shaoxiong Ji, Sophia Ananiadou
Publisher: arXiv
This survey provides a comprehensive overview of approaches to mental illness detection in social media that incorporate emotion fusion, discussing fusion strategies and challenges.
Access Paper →Detecting Mental Disorders in Social Media Using a Multichannel Representation
Authors: Mario Ezra Aragón, Antonio Paredes-López, Manuel Montes-y-Gómez, Hugo Jair Escalante
Publisher: ResearchGate
This study proposes a novel model that extracts different views from posts shared by users, including thematic interests, writing style, and emotions, to detect mental disorders.
Access Paper →Social Media and Mental Health: Benefits, Risks, and Opportunities for Research and Practice
Authors: Lauren A. Magruder, Emily L. Haroz, Paul Bolton, Laura K. Murray, Judith K. Bass
Publisher: PubMed Central
This article considers the role of social media as a potentially viable intervention platform for offering support to persons with mental disorders, promoting engagement and retention in care.
Access Paper →A Deep Learning Model for Detecting Mental Illness from User Content on Social Media
Authors: Not specified
Publisher: Nature
This study developed a deep learning model to identify a user’s mental state based on their posting information on social media.
Access Paper →Social Media Emotional Patterns for Detecting Mental Disorders
Authors: Not specified
Publisher: International Journal of Creative Research Thoughts
This study estimates computational representations aiming to design the presence and changes of emotions communicated by social media users to detect mental disorders.
Access Paper →Detecting Mental Illnesses in Social Networks through Psychological Patterns
Authors: Not specified
Publisher: Cuestiones de Fisioterapia
This study explores the use of emotional patterns on social media platforms as a means of identifying people with anorexia.
Access Paper →Exploring Emotional Patterns in Social Media through NLP Models to Detect Mental Health Disorders
Authors: Not specified
Publisher: PubMed Central
This study aimed to develop an advanced ensemble approach for automated classification of mental health disorders in social media posts using NLP models.
Access Paper →Emotion-Based Modeling of Mental Disorders on Social Media
Authors: Xiaobo Guo, Yaojia Sun, Soroush Vosoughi
Publisher: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
This paper proposes a model for passively detecting mental disorders by analyzing emotional states and their transitions in Reddit conversations.
Access Paper →Emotion Fusion for Mental Illness Detection from Social Media: A Survey
Authors: Tianlin Zhang, Kailai Yang, Shaoxiong Ji, Sophia Ananiadou
Publisher: arXiv
This survey provides a comprehensive overview of approaches to mental illness detection in social media that incorporate emotion fusion, discussing fusion strategies and challenges.
Access Paper →These papers represent the latest advancements in AI-driven mental health research from the global community.
Research Highlights
Emotion Detection
Advanced NLP models now detect emotional shifts with 85% accuracy, aiding early intervention (IEEE, 2021).
Social Media Insights
Analysis of Reddit data reveals 20% higher detection rates for depression (ACM, 2021).
Transformer Models
RoBERTa-based systems classify mental illnesses with 90% precision (arXiv, 2020).