An iterative topic model filtering framework for short and noisy user-generated data: analyzing conspiracy theories on twitter.

Kant G, Wiebelt L, Weisser C, Kis-Katos K, Luber M, Säfken B

DOI: 10.1007/s41060-022-00321-4
Published: 2022 May 6
Container: International journal of data science and analytics
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1007/s41060-022-00321-4,
  title = {An iterative topic model filtering framework for short and noisy user-generated data: analyzing conspiracy theories on twitter.},
  author = {Kant G and Wiebelt L and Weisser C and Kis-Katos K and Luber M and Säfken B},
  year = {2022},
  journal = {International journal of data science and analytics},
  doi = {10.1007/s41060-022-00321-4},
  url = {https://doi.org/10.1007/s41060-022-00321-4}
}

RIS

TY  - JOUR
TI  - An iterative topic model filtering framework for short and noisy user-generated data: analyzing conspiracy theories on twitter.
AU  - Kant G
AU  - Wiebelt L
AU  - Weisser C
AU  - Kis-Katos K
AU  - Luber M
AU  - Säfken B
PY  - 2022
JO  - International journal of data science and analytics
DO  - 10.1007/s41060-022-00321-4
UR  - https://doi.org/10.1007/s41060-022-00321-4
ER  -

APA

G, K., L, W., C, W., K, K., M, L., & B, S. (2022). An iterative topic model filtering framework for short and noisy user-generated data: analyzing conspiracy theories on twitter.. International journal of data science and analytics. https://doi.org/10.1007/s41060-022-00321-4

Source records

pubmed · retrieved 2026-09-26T10:11:21.563Z