ArticleHeliyon2025
Fake news, real needs: A qualitative study on Sino-Japanese theurgy fighting.
Article in Heliyon, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mainstream fake news research has previously mostly focused on authenticity but often overlooked the individual psychological needs and actual social issues behind fake news. These limitations impede a profound understanding of its societal intricacies. This study aims to excavate deeper into the latent significance of fake news, peering through its lens to unearth hidden group psychology and realistic needs. Comments about "Sino-Japanese theurgy fighting" (purportedly a fight between Chinese Taoist priests and Japanese Yin-Yang masters) on Chinese TikTok was selected to conduct content analysis and erect a theoretical model using Grounded Theory. Based on the analysis of 1933 valid comments from 29 videos, it is found that support for fake news correlates not just with judgment of authenticity, but also with psychological needs such as emotional catharsis and position identification. This shift in research perspective enriches our comprehension and offers a novel angle for addressing fake news. This finding reflects the attitudes and behavior patterns of social groups in specific situations and are of great significance for understanding the formation and dissemination of public opinion. Thus, in this special "post-truth" era, emotion regulation and fact clarification are equally essential.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.