# A Rare Topic Discovery Model for Short Texts Based on Co-occurrence word Network

arXiv: https://arxiv.org/abs/2207.00432 · HTML: https://mlsanity.com/paper/2207.00432/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 36 references that are in the map.
- First posted on arXiv 30 Jun 2022.

## Builds on (most cited first, 5 of 5)

- [Short Text Topic Modeling Techniques, Applications, and Performance: A Survey](https://mlsanity.com/paper/1904.07695/) (2019)
- [Scientific and Technological Information Oriented Semantics-adversarial and Media-adversarial Cross-media Retrieval](https://mlsanity.com/paper/2203.08615/) (2022)
- [Context Reinforced Neural Topic Modeling over Short Texts](https://mlsanity.com/paper/2008.04545/) (2020)
- [Word Network Topic Model: A Simple but General Solution for Short and Imbalanced Texts](https://mlsanity.com/paper/1412.5404/) (2014)
- [STTM: A Tool for Short Text Topic Modeling](https://mlsanity.com/paper/1808.02215/) (2018)

## Abstract (arXiv)

We provide a simple and general solution for the discovery of scarce topics in unbalanced short-text datasets, namely, a word co-occurrence network-based model CWIBTD, which can simultaneously address the sparsity and unbalance of short-text topics and attenuate the effect of occasional pairwise occurrences of words, allowing the model to focus more on the discovery of scarce topics. Unlike previous approaches, CWIBTD uses co-occurrence word networks to model the topic distribution of each word, which improves the semantic density of the data space and ensures its sensitivity in identify-ing rare topics by improving the way node activity is calculated and normal-izing scarce topics and large topics to some extent. In addition, using the same Gibbs sampling as LDA makes CWIBTD easy to be extended to vari-ous application scenarios. Extensive experimental validation in the unbal-anced short text dataset confirms the superiority of CWIBTD over the base-line approach in discovering rare topics. Our model can be used for early and accurate discovery of emerging topics or unexpected events on social platforms.