Cargando…
Machine Learning Methods to Predict Social Media Disaster Rumor Refuters
This research provides a general methodology for distinguishing disaster-related anti-rumor spreaders from a non-ignorant population base, with strong connections in their social circle. Several important influencing factors are examined and illustrated. User information from the most recent posted...
Autores principales: | Wang, Shihang, Li, Zongmin, Wang, Yuhong, Zhang, Qi |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6518238/ https://www.ncbi.nlm.nih.gov/pubmed/31022894 http://dx.doi.org/10.3390/ijerph16081452 |
Ejemplares similares
-
Configurational patterns for COVID-19 related social media rumor refutation effectiveness enhancement based on machine learning and fsQCA
por: Li, Zongmin, et al.
Publicado: (2023) -
A model and simulation of the emotional contagion of netizens in the process of rumor refutation
por: Zeng, Runxi, et al.
Publicado: (2019) -
Dissemination and Refutation of Rumors During the COVID-19 Outbreak in China: Infodemiology Study
por: Chen, Bin, et al.
Publicado: (2021) -
Starts and refutations of the Covid-19 rumors: Evidence from the reaction of the stock market
por: Li, Zhe, et al.
Publicado: (2022) -
Fighting rumors to fight COVID-19: Investigating rumor belief and sharing on social media during the pandemic
por: Guo, Feng, et al.
Publicado: (2023)