# Unveiling the Potential of BERTopic for Multilingual Fake News Analysis -- Use Case: Covid-19

arXiv: https://arxiv.org/abs/2407.08417 · HTML: https://mlsanity.com/paper/2407.08417/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 31 references that are in the map.
- First posted on arXiv 11 Jul 2024.

## Builds on (most cited first, 8 of 8)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [FakeCovid -- A Multilingual Cross-domain Fact Check News Dataset for COVID-19](https://mlsanity.com/paper/2006.11343/) (2020)
- [Contextualized Topic Coherence Metrics](https://mlsanity.com/paper/2305.14587/) (2023)
- [Public sentiment analysis and topic modeling regarding COVID-19 vaccines on the Reddit social media platform: A call to action for strengthening vaccine confidence](https://mlsanity.com/paper/2108.13293/) (2021)
- [Dataset of Fake News Detection and Fact Verification: A Survey](https://mlsanity.com/paper/2111.03299/) (2021)
- [Sentiment Analysis and Topic Modeling for COVID-19 Vaccine Discussions](https://mlsanity.com/paper/2111.04415/) (2021)
- [Revisiting Automated Topic Model Evaluation with Large Language Models](https://mlsanity.com/paper/2305.12152/) (2023)

## Built on by (most cited first, 1 of 1)

- [LLM-based Detection of Manipulative Political Narratives](https://mlsanity.com/paper/2605.14354/) (2026)

## Abstract (arXiv)

Topic modeling is frequently being used for analysing large text corpora such as news articles or social media data. BERTopic, consisting of sentence embedding, dimension reduction, clustering, and topic extraction, is the newest and currently the SOTA topic modeling method. However, current topic modeling methods have room for improvement because, as unsupervised methods, they require careful tuning and selection of hyperparameters, e.g., for dimension reduction and clustering. This paper aims to analyse the technical application of BERTopic in practice. For this purpose, it compares and selects different methods and hyperparameters for each stage of BERTopic through density based clustering validation and six different topic coherence measures. Moreover, it also aims to analyse the results of topic modeling on real world data as a use case. For this purpose, the German fake news dataset (GermanFakeNCovid) on Covid-19 was created by us and in order to experiment with topic modeling in a multilingual (English and German) setting combined with the FakeCovid dataset. With the final results, we were able to determine thematic similarities between the United States and Germany. Whereas, distinguishing the topics of fake news from India proved to be more challenging.