# A Human Word Association based model for topic detection in social networks

arXiv: https://arxiv.org/abs/2301.13066 · HTML: https://mlsanity.com/paper/2301.13066/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 43 references that are in the map.
- First posted on arXiv 30 Jan 2023.

## Builds on (most cited first, 6 of 6)

- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [TopicBERT: A Transformer transfer learning based memory-graph approach for multimodal streaming social media topic detection](https://mlsanity.com/paper/2008.06877/) (2020)
- [Covid-Transformer: Detecting COVID-19 Trending Topics on Twitter Using Universal Sentence Encoder](https://mlsanity.com/paper/2009.03947/) (2020)
- [Event Detection in Twitter Stream using Weighted Dynamic Heartbeat Graph Approach](https://mlsanity.com/paper/1902.08522/) (2019)
- [Multimodal price prediction](https://mlsanity.com/paper/2007.05056/) (2020)
- [A Semantic Modular Framework for Events Topic Modeling in Social Media](https://mlsanity.com/paper/2301.09009/) (2023)

## Built on by (most cited first, 2 of 2)

- [Persian topic detection based on Human Word association and graph embedding](https://mlsanity.com/paper/2302.09775/) (2023)
- [A comprehensive study on Frequent Pattern Mining and Clustering categories for topic detection in Persian text stream](https://mlsanity.com/paper/2403.10237/) (2024)

## Abstract (arXiv)

With the widespread use of social networks, detecting the topics discussed on these platforms has become a significant challenge. Current approaches primarily rely on frequent pattern mining or semantic relations, often neglecting the structure of the language. Language structural methods aim to discover the relationships between words and how humans understand them. Therefore, this paper introduces a topic detection framework for social networks based on the concept of imitating the mental ability of word association. This framework employs the Human Word Association method and includes a specially designed extraction algorithm. The performance of this method is evaluated using the FA-CUP dataset, a benchmark in the field of topic detection. The results indicate that the proposed method significantly improves topic detection compared to other methods, as evidenced by Topic-recall and the keyword F1 measure. Additionally, to assess the applicability and generalizability of the proposed method, a dataset of Telegram posts in the Persian language is used. The results demonstrate that this method outperforms other topic detection methods.