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The delayed and combinatorial response of online public opinion to the real world: An inquiry into news texts during the COVID-19 era

Social Work

The delayed and combinatorial response of online public opinion to the real world: An inquiry into news texts during the COVID-19 era

Y. Du, H. Cheng, et al.

Discover how online public opinion reacts over time to the COVID-19 pandemic in this fascinating study conducted by Yamin Du, Huanhuan Cheng, Qing Liu, and Song Tan. Using advanced techniques like natural language processing and machine learning, the research unveils surprising patterns of public sentiment that lag behind actual pandemic events.

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Playback language: English
Abstract
This study explores the delayed and combinatory responses of online public opinion to the intensity of the COVID-19 pandemic using natural language processing, statistical analysis, and machine learning-based causal inference. Findings indicate a long-term lag in online public opinion's response, with identical COVID-19 intensity data triggering multiple delayed responses and a single delayed opinion datum influenced by multiple preceding data points. This results in a waveform structure of real-world impacts influenced by online public opinion, with sensitivity varying across different time periods.
Publisher
HUMANITIES AND SOCIAL SCIENCES COMMUNICATIONS
Published On
Aug 10, 2024
Authors
Yamin Du, Huanhuan Cheng, Qing Liu, Song Tan
Tags
public opinion
COVID-19
natural language processing
machine learning
causal inference
statistical analysis
delayed responses

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