# Graph-of-Tweets: A Graph Merging Approach to Sub-event Identification

arXiv: https://arxiv.org/abs/2101.03208 · HTML: https://mlsanity.com/paper/2101.03208/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 8 Jan 2021.

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

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/1809.05679/) (2018)
- [Graph-based Neural Multi-Document Summarization](https://mlsanity.com/paper/1706.06681/) (2017)
- [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)
- [Extract with Order for Coherent Multi-Document Summarization](https://mlsanity.com/paper/1706.06542/) (2017)
- [An Event Detection Approach Based On Twitter Hashtags](https://mlsanity.com/paper/1804.11243/) (2018)

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

- [Tracing Topic Transitions with Temporal Graph Clusters](https://mlsanity.com/paper/2104.07836/) (2021)

## Abstract (arXiv)

Graph structures are powerful tools for modeling the relationships between textual elements. Graph-of-Words (GoW) has been adopted in many Natural Language tasks to encode the association between terms. However, GoW provides few document-level relationships in cases when the connections between documents are also essential. For identifying sub-events on social media like Twitter, features from both word- and document-level can be useful as they supply different information of the event. We propose a hybrid Graph-of-Tweets (GoT) model which combines the word- and document-level structures for modeling Tweets. To compress large amount of raw data, we propose a graph merging method which utilizes FastText word embeddings to reduce the GoW. Furthermore, we present a novel method to construct GoT with the reduced GoW and a Mutual Information (MI) measure. Finally, we identify maximal cliques to extract popular sub-events. Our model showed promising results on condensing lexical-level information and capturing keywords of sub-events.