Topic modeling using Twitter messages related to cervical cancer
DOI:
https://doi.org/10.26439/interfases2022.n016.5887Keywords:
text mining, Twitter, cervical cancer, topic modelingAbstract
As a global health problem, cervical cancer generates much information that circulates through social networks. Modeling allows us to automatically identify the topics that deal with a specific subject matter in a set of documents. This research used the LDA algorithm and the coherence metric for topic modeling and identified seven topics in a set of tweets on cervical cancer. The topics were related to the effect of HPV vaccines, the relationship between HPV and other diseases, forms of prevention such as vaccines and Papanicolaou tests, programs that provide medical services for the prevention and elimination of this disease, stories of women who have had cervical cancer and studies aimed at Latina women.
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