Fairness modeling for topics with different scales in short texts.

ABSTRACT: The application of topic modeling to short texts is beset by challenges such as data sparsity and an absence of contextual information. Traditional research methods tend to prioritise high-attention and popular topics, frequently overlooking the identification of emerging topics. Consequently, subjects of a minor scale are prone to being overlooked during the topic identification process. Furthermore, in the context of topic modelling, information that varies in terms of the attention it receives is not treated equally. In order to address the aforementioned issues, a fairness-oriented topic discovery approach (MixTM-G) is proposed. This approach has been designed to facilitate the discovery of topics with different levels of attention. The proposed methodology involves the integration of

SUBMITTER: Zhu C 

PROVIDER: S-EPMC12453639 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature