Home ›
Entities
› academia
› Identifying Covid-19 misinformation tweets and learning their spatio-temporal topic dynamics using Nonnegative Coupled Matrix Tensor Factorization
Identifying Covid-19 misinformation tweets and learning their spatio-temporal topic dynamics using Nonnegative Coupled Matrix Tensor Factorization
Research article (Social Network Analysis and Mining, 2021) · cited 23× · AI/ML
Identifying Covid-19 misinformation tweets and learning their spatio-temporal topic dynamics using Nonnegative Coupled Matrix Tensor Factorization
Summary
Identifying Covid-19 misinformation tweets and learning their spatio-temporal topic dynamics using Nonnegative Coupled Matrix Tensor Factorization is a scholarly article[1].
Key Facts
Identifying Covid-19 misinformation tweets and learning their spatio-temporal topic dynamics using Nonnegative Coupled Matrix Tensor Factorization's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Identifying Covid-19 misinformation tweets and learning their spatio-temporal topic dynamics using Nonnegative Coupled Matrix Tensor Factorization. Retrieved May 24, 2026, from https://4ort.xyz/entity/identifying-covid-19-misinformation-tweets-and-learning-their-spatio-temporal-topic-dynamics-using-nonnegative-coupled-m
MLA“Identifying Covid-19 misinformation tweets and learning their spatio-temporal topic dynamics using Nonnegative Coupled Matrix Tensor Factorization.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/identifying-covid-19-misinformation-tweets-and-learning-their-spatio-temporal-topic-dynamics-using-nonnegative-coupled-m.
BibTeX@misc{4ortxyz_identifying-covid-19-misinformation-tweets-and-learning-their-spatio-temporal-topic-dynamics-using-nonnegative-coupled-m_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Identifying Covid-19 misinformation tweets and learning their spatio-temporal topic dynamics using Nonnegative Coupled Matrix Tensor Factorization}}, year = {2026}, url = {https://4ort.xyz/entity/identifying-covid-19-misinformation-tweets-and-learning-their-spatio-temporal-topic-dynamics-using-nonnegative-coupled-m}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Identifying Covid-19 misinformation tweets and learning their spatio-temporal topic dynamics using Nonnegative Coupled Matrix Tensor Factorization — https://4ort.xyz/entity/identifying-covid-19-misinformation-tweets-and-learning-their-spatio-temporal-topic-dynamics-using-nonnegative-coupled-m (retrieved 2026-05-24)
