Early Discovery of Emerging Entities in Persian Twitter with Semantic Similarity
This addresses the need for early detection of emerging entities on social media for individuals, companies, and governments, but it is incremental as it builds on existing research with a focus on Persian data.
The paper tackles the problem of discovering emerging entities (EEs) in Persian Twitter before their establishment, proposing EEPT, an online clustering method that requires no training data and uses a new evaluation metric, with results showing it is promising and finds significant entities early.
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.