# A Semantic Modular Framework for Events Topic Modeling in Social Media

arXiv: https://arxiv.org/abs/2301.09009 · HTML: https://mlsanity.com/paper/2301.09009/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 51 references that are in the map.
- First posted on arXiv 21 Jan 2023.

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Deep Reinforcement Learning using Genetic Algorithm for Parameter Optimization](https://mlsanity.com/paper/1905.04100/) (2019)
- [Real-time Event Detection on Social Data Streams](https://mlsanity.com/paper/1907.11229/) (2019)
- [Bidirectional Recurrent Neural Networks for Medical Event Detection in Electronic Health Records](https://mlsanity.com/paper/1606.07953/) (2016)
- [Embed2Detect: Temporally Clustered Embedded Words for Event Detection in Social Media](https://mlsanity.com/paper/2006.05908/) (2020)
- [TopicBERT: A Transformer transfer learning based memory-graph approach for multimodal streaming social media topic detection](https://mlsanity.com/paper/2008.06877/) (2020)
- [Extracting News Events from Microblogs](https://mlsanity.com/paper/1806.07573/) (2018)

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

- [A Human Word Association based model for topic detection in social networks](https://mlsanity.com/paper/2301.13066/) (2023)

## Abstract (arXiv)

The advancement of social media contributes to the growing amount of content they share frequently. This framework provides a sophisticated place for people to report various real-life events. Detecting these events with the help of natural language processing has received researchers' attention, and various algorithms have been developed for this goal. In this paper, we propose a Semantic Modular Model (SMM) consisting of 5 different modules, namely Distributional Denoising Autoencoder, Incremental Clustering, Semantic Denoising, Defragmentation, and Ranking and Processing. The proposed model aims to (1) cluster various documents and ignore the documents that might not contribute to the identification of events, (2) identify more important and descriptive keywords. Compared to the state-of-the-art methods, the results show that the proposed model has a higher performance in identifying events with lower ranks and extracting keywords for more important events in three English Twitter datasets: FACup, SuperTuesday, and USElection. The proposed method outperformed the best reported results in the mean keyword-precision metric by 7.9%.