# Early Discovery of Emerging Entities in Persian Twitter with Semantic Similarity

arXiv: https://arxiv.org/abs/2207.02434 · HTML: https://mlsanity.com/paper/2207.02434/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 6 Jul 2022.

## Builds on (most cited first, 3 of 3)

- [Real-time Event Detection on Social Data Streams](https://mlsanity.com/paper/1907.11229/) (2019)
- [The Birth of Collective Memories: Analyzing Emerging Entities in Text Streams](https://mlsanity.com/paper/1701.04039/) (2017)
- [Early Discovery of Emerging Entities in Microblogs](https://mlsanity.com/paper/1907.03513/) (2019)

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

- [Emotion Alignment: Discovering the Gap Between Social Media and Real-World Sentiments in Persian Tweets and Images](https://mlsanity.com/paper/2504.10662/) (2025)

## Abstract (arXiv)

Discovering emerging entities (EEs) is the problem of finding entities before their establishment. These entities can be critical for individuals, companies, and governments. Many of these entities can be discovered on social media platforms, e.g. Twitter. These identities have been the spot of research in academia and industry in recent years. Similar to any machine learning problem, data availability is one of the major challenges in this problem. This paper proposes EEPT. That is an online clustering method able to discover EEs without any need for training on a dataset. Additionally, due to the lack of a proper evaluation metric, this paper uses a new metric to evaluate the results. The results show that EEPT is promising and finds significant entities before their establishment.