Tracking Fine-Grained Public Opinions: Two Datasets from Online Discourse on Trending Topics

Haihua Xie () and Miao He
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Haihua Xie: Beijing Institute of Mathematical Sciences and Applications (BIMSA), Beijing 101408, China
Miao He: Beijing Institute of Mathematical Sciences and Applications (BIMSA), Beijing 101408, China

Mathematics, 2025, vol. 13, issue 21, 1-24

Abstract: In this paper, we introduce two novel, publicly available datasets that capture public opinion on two highly salient and timely topics: the perceived feasibility of artificial general intelligence (AGI) and the Hamas–Israel conflict (HIC). Collected from social media posts on X(formerly Twitter), the datasets were curated and annotated using a structured methodology designed to ensure robustness, consistency, and interpretability. Whereas prior research on public opinion has primarily relied on theoretical models, controlled experiments, or narrowly scoped datasets, such approaches often fail to capture the complexity and dynamism of real-world discourse. In contrast, the datasets presented in this work provide temporally fine-grained, large-scale empirical data that reflect the evolving nature of public sentiment on contemporary global issues. In addition to detailed sentiment annotations, these resources support longitudinal analyses of opinion dynamics, offering a foundation for empirical studies on consensus formation, polarization, and social influence in digital environments. By releasing these datasets, we aim to advance the empirical study of public opinion in the age of social media.

Keywords: public opinion; fine-grained sentiment analysis; social computing; natural language processing; Twitter dataset; opinion dynamics (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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