Projects / Top Talent Project

Leveraging AI and NLP for Real-Time Youth Mental Health Monitoring

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This is a paid opportunity. In order to be eligible to apply for this project, you need to be part of the Omdena community and have finished at least one AI Innovation Challenge.

You can find our upcoming AI Innovation Challenges at

The problem

The rise of social media and search platforms has brought both opportunities and challenges in terms of mental health. While these platforms provide an avenue for connecting with others and accessing information, they can also exacerbate mental health issues through cyberbullying, social comparison, and exposure to distressing content. Additionally, the extensive use of social media can lead to a sense of social pressure and the perpetuation of unrealistic standards, contributing to anxiety and depression among young individuals.

One of the key problems faced in addressing youth mental health is the lack of efficient and personalized support systems. Many young people may not recognize their mental health issues or may be hesitant to seek help due to stigma or a lack of awareness about available resources. Traditional mental health services may not always be easily accessible or tailored to the specific needs of young individuals, leading to underutilization of available support.

Impact of the problem:

The impact of unaddressed youth mental health issues is profound and far-reaching. Mental health challenges during adolescence and early adulthood can have long-term consequences, affecting educational attainment, career opportunities, and overall lower life satisfaction. Untreated mental health conditions can also lead to increased healthcare costs and higher strain on families and communities.

Moreover, the lack of efficient and accessible support systems for youth mental health contributes to a significant public health burden. The mental well-being of the younger generation is essential for societal progress and economic growth. Neglecting mental health concerns in young individuals can lead to a generation of individuals facing emotional distress and significant challenges in achieving their full potential.


This project aims to tackle these challenges by utilizing data from social media and search platforms to uncover trends in youth mental health sentiment and modeling. By analyzing the language and content used by young individuals on these platforms, the project can identify patterns indicative of mental health issues, providing valuable insights into the emotional well-being of youth.

Additionally, this project aims to inform the future development of a discovery tool that recommends appropriate mental health services and resources to young users based on their identified needs. This personalized approach seeks to break down barriers to accessing mental health support and increase awareness about available services by understanding youth on a deeper level.

Overall, the project’s goal is to contribute to a more proactive and holistic approach to youth mental health. By leveraging technology and data analysis, the project seeks to promote early intervention, reduce stigma, and empower young individuals to take charge of their mental well-being. Ultimately, addressing youth mental health challenges can lead to a healthier and more resilient generation, benefiting society as a whole.

The project goals

The main goal of the project is to uncover trends (sentiment, modeling) in youth mental health and an insight tool for service providers so that they can proactively address emerging trends. It will build automation to extract information and add it to the Knowledge Graph from different data sources.


  • Collecting data on a country level and regionally from different social media platforms.
  • Understanding the trends of people causing mental health issues.
  • Dashboards for trend analysis: Develop dashboards that leverage public datasets and APIs to identify trending topics and issues through web traffic, social platforms, and search engines. Apply methods like sentiment analysis, topic modeling, and entity recognition to extract meaningful insights from social media posts, forum discussions, and search queries related to mental health.
  • Integration with mental health service data: Integrate anonymized available service data, such as user queries, helpline calls, or counseling session records, with the trend analysis. This will enable us to identify overlaps between online trends and the issues youth are reaching out to your services about. 
  • Documentation: Delivering comprehensive documentation that ensures the work and code can be understood, maintained, and improved independently. This includes clear explanations of objectives, methodologies, and design choices, as well as well-commented code for readability and maintainability. The documentation enables seamless knowledge transfer to future teams, empowering them to build upon our achievements and advance the project.

**More details will be shared with the selected team.

Why join? The uniqueness of Omdena Top Talent Projects

Top Talent opportunities come as a natural next step after participating in Omdena’s AI Innovation Challenges.

Everyone in the community is eligible to participate once they have shown the relevant skills based on the merit of involvement in past Omdena challenges and the community.

If you are looking for the next challenge after participating in one or more Omdena AI Innovation Challenges, then we believe you have made the right choice! With a healthy, pressured environment, you will be pushed to contribute, learn and grow even more.

Find more information on how an Omdena Top Talent Program works

First Omdena Project?

Join the Omdena community to make a real-world impact and develop your career

Build a global network and get mentoring support

Earn money through paid gigs and access many more opportunities

Eligibility to join an Omdena Top Talent project

Finished at least one AI Innovation Challenge

Received a recommendation from the Omdena Core Team Member/ Project Owner (PO) is a plus

Skill requirements

Good English

Machine Learning Engineer

Experience working with NLP is a plus.

This challenge is hosted with our friends at

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