Understanding the Sentiments and Aspirations of Young People
Together with Fondation Botner, a global team of 50 changemakers collaborated to capture and understand what young people (age 10-24 yrs) today think about topics like their future, aspirations, concerns, and challenges they face, etc. The project host, Fondation Botnar, is a Swiss-based foundation that champions the use of digital and AI to improve the health and wellbeing of children and young people globally.
The project goal: Youth empowerment
Every day, millions of young people react to new events, ideas, and reforms online. Whether it’s adding their voice to a movement on Twitter or Facebook, staying up to date with the latest news and contributing to discussions on Reddit, or engaging in challenges on Tik Tok, the digital world is the place where many conversations and interactions take place. This digital ecosystem offers an excellent opportunity for analysis to understand what young people’s fears, dreams, and thoughts are on different topics
Solutions and results
Some of the first insights about mental health gathered through this project shows that self-harm and being liked, and unliked, were two of the most common topics of concern discussed in 2019. In 2020, with the onset of COVID-19, the conversations shifted to focus on the pandemic and loneliness. Young people in all parts of the world appear to be concerned with education, and in Africa, many young people are also additionally concerned about HIV.
With so many conversations taking place online, and especially during the ongoing pandemic, social media offers great potential for generating insights about the conversations young people are having. The insights are generated from social media through a process that includes gathering, analyzing, and presentation of patterns of anonymized data. The teams developed AI systems to analyze and understand data from various sources.
From a technical perspective, different task groups applied methodologies like sentiment analysis, a.k.a opinion mining, or emotional artificial intelligence, which uses text analysis, and natural language processing to identify affective level patterns presented in data. We also applied empath analysis to connect the text within a wide range of sentiments besides just negative, neutral, and positive polarities. To draw more insights we scrapped and processed news articles from different resources and applied topic modeling, in order to identify frequently discussed topics and useful insights.
The findings will help Fondation Botnar better understand how to support young people more effectively and catalyze appropriate initiatives for youth empowerment.
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Thank you to all collaborators. We were really stimulated by the process, which gives us much to reflect on.