# Tracing Topic Transitions with Temporal Graph Clusters

arXiv: https://arxiv.org/abs/2104.07836 · HTML: https://mlsanity.com/paper/2104.07836/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 16 Apr 2021.

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

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Real-time Event Detection on Social Data Streams](https://mlsanity.com/paper/1907.11229/) (2019)
- [Tweet2Vec: Character-Based Distributed Representations for Social Media](https://mlsanity.com/paper/1605.03481/) (2016)
- [Event detection, tracking, and visualization in Twitter: a mention-anomaly-based approach](https://mlsanity.com/paper/1505.05657/) (2015)
- [An Event Detection Approach Based On Twitter Hashtags](https://mlsanity.com/paper/1804.11243/) (2018)
- [Graph-of-Tweets: A Graph Merging Approach to Sub-event Identification](https://mlsanity.com/paper/2101.03208/) (2021)

## Abstract (arXiv)

Twitter serves as a data source for many Natural Language Processing (NLP) tasks. It can be challenging to identify topics on Twitter due to continuous updating data stream. In this paper, we present an unsupervised graph based framework to identify the evolution of sub-topics within two weeks of real-world Twitter data. We first employ a Markov Clustering Algorithm (MCL) with a node removal method to identify optimal graph clusters from temporal Graph-of-Words (GoW). Subsequently, we model the clustering transitions between the temporal graphs to identify the topic evolution. Finally, the transition flows generated from both computational approach and human annotations are compared to ensure the validity of our framework.