Abstract
Investing in children and youth is a critical step towards inclusive, equitable, and sustainable development for current and future generations. Several international agendas for accomplishing common global goals emphasize the need for active youth participation and engagement for sustainable development. The 2030 Agenda for Sustainable Development emphasizes the need for youth engagement and the inclusion of youth perspectives as an important step toward addressing each of the 17 Sustainable Development Goals. The aim of this study is to analyze youth perspectives, values, and sentiments towards issues addressed by the 17 Sustainable Development Goals through social network analysis using machine learning. Social network data collected during 7 major sustainability conferences aimed at engaging children and youth is analyzed using natural language processing techniques for sentiment analysis. This data categorized using a natural language processing text classifier trained on a sample dataset of social network data during the 7 youth sustainability conferences for deeper understanding of youth perspectives in relation to the SDGs. Machine learning identified demographic and location attributes and features are utilized in order to identify bias and demographic differences between ages, gender, and race among youth. Using natural language processing, the qualitative data collected from over 7 different countries in 3 languages are systematically translated, categorized, and analyzed, revealing key trends and focus areas for sustainable youth development policies. The obtained results reveal the general youth's depth of knowledge on sustainable development and their attitudes towards each of the 17 SDGs. The findings of this study serve as a guide toward better understanding the interests, roles, and perspectives of children and youth in achieving the goals of Agenda 2030.
Abstract (translated)
URL
https://arxiv.org/abs/2211.14321
