mlsanityData to 14 Oct 2026

Improving Topic Modeling of Social Media Short Texts with Rephrasing: A Case Study of COVID-19 Related Tweets

arXiv 2510.18908 ↗First posted 21 Oct 2025cs.CL cs.AIcited by 3

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Abstract (arXiv)

Social media platforms such as Twitter (now X) provide rich data for analyzing public discourse, especially during crises such as the COVID-19 pandemic. However, the brevity, informality, and noise of social media short texts often hinder the effectiveness of traditional topic modeling, producing incoherent or redundant topics that are often difficult to interpret. To address these challenges, we have developed TM-Rephrase, a model-agnostic framework that leverages large language models (LLMs) to rephrase raw tweets into more standardized and formal language prior to topic modeling. Using a dataset of 25,027 COVID-19-related Twitter posts, we investigate the effects of two rephrasing strategies, general- and colloquial-to-formal-rephrasing, on multiple topic modeling methods. Results demonstrate that TM-Rephrase improves three metrics measuring topic modeling performance (i.e., topic coherence, topic uniqueness, and topic diversity) while reducing topic redundancy of most topic modeling algorithms, with the colloquial-to-formal strategy yielding the greatest performance gains and especially for the Latent Dirichlet Allocation (LDA) algorithm. This study contributes to a model-agnostic approach to enhancing topic modeling in public health related social media analysis, with broad implications for improved understanding of public discourse in health crisis as well as other important domains.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 13 of its 46 references that are in the map. First posted on arXiv 21 Oct 2025.

Builds on

13 of 46 references.

References · 13

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  2. 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 801
  3. 2019Topic Modeling in Embedding Spacescited by 224
  4. 2024The Effect of Sampling Temperature on Problem Solving in Large Language Modelscited by 170
  5. 2024Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modelingcited by 161
  6. 2023Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Surveycited by 135
  7. 2020Top2Vec: Distributed Representations of Topicscited by 126
  8. 2019Topic Modeling with Wasserstein Autoencoderscited by 53
  9. 2022Mitigating Data Sparsity for Short Text Topic Modeling by Topic-Semantic Contrastive Learningcited by 38
  10. 2024FASTopic: Pretrained Transformer is a Fast, Adaptive, Stable, and Transferable Topic Modelcited by 35
  11. 2024Deliberate Reasoning in Language Models as Structure-Aware Planning with an Accurate World Modelcited by 23
  12. 2025Interpretable Depression Detection from Social Media Text Using LLM-Derived Embeddingscited by 6
1 more
  1. 2025Digital Voices of Survival: From Social Media Disclosures to Support Provisions for Domestic Violence Victimscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025ChromouVQA: Benchmarking Vision-Language Models under Chromatic Camouflaged Imagescited by 1
  2. 2026MedRAGChecker: Claim-Level Verification for Biomedical Retrieval-Augmented Generationcited by 1
  3. 2026Document Topic Alignment Metrics for Evaluating Topic Models of Short-Text Public Health Communications on Social Mediacited by 0