Understanding public sentiments and misbeliefs about Sustainable Development Goals: a sentiment and topic modeling analysis

Abstract

Purpose

Misinformation surrounding the Sustainable Development Goals (SDGs) has contributed to the formation of misbeliefs among the public. The purpose of this paper is to investigate public sentiment and misbeliefs about the SDGs on the YouTube platform.

Design/methodology/approach

The authors extracted 8,016 comments from YouTube videos associated with SDGs. The authors used a pre-trained Python library NRC lexicon for sentiment and emotion analysis, and to extract latent topics, the authors used BERTopic for topic modeling.

Findings

The authors found eight emotions, with negativity outweighing positivity, in the comment section. In addition, the authors identified the top 20 topics discussing various SDGs and SDG-related misbeliefs.

Practical implications

The authors reported topics related to public misbeliefs about SDGs and associated keywords. These keywords can be used to formulate social media content moderation strategies to screen out content that creates these misbeliefs. The result of hierarchical clustering can be used to devise and optimize response strategies by governments and policymakers to counter public misbeliefs.

Originality/value

This study represents an initial endeavor to gain a deeper understanding of the public’s misbeliefs regarding SDGs. The authors identified novel misbeliefs about SDGs that previous literature has not studied. Furthermore, the authors introduce an algorithm BERTopic for topic modeling that leverages transformer architecture for context-aware topic modeling.

Citation

Verma, A. and Nayak, J.K. (2024), "Understanding public sentiments and misbeliefs about Sustainable Development Goals: a sentiment and topic modeling analysis", Journal of Information, Communication and Ethics in Society, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JICES-05-2023-0073