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2 2023

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Towards explainability of hashtags in the light of Graph Spectral Clustering methods

Starosta Bartłomiej, Kłopotek Mieczysław A., Wierzchoń Sławomir T.

Studia Informatica : systems and information technology

2023

Vol. 2(29)

57--68

Hashtags constitute an indispensable part of modern social media world. As more and more hashtags are invented, it becomes a necessity to create clusters of these hashtags. Nowadays, however, the clustering alone does not help the users. They are asking for justification or expressed in the modern AI language, the clustering has to be explainable. We discuss a novel approach to hashtag explanation via a measure of similarity between hashtags based on the Graph Spectral Analysis. The application of this similarity measure may go far beyond the classical clustering task. It can be used to provide with explanations for the hashtags. In this paper we propose such a novel view of the proposed hashtag similarity measure.

Hashtag Discernability - Competitiveness Study of Graph Spectral and Other Clustering Methods

Starosta Bartłomiej, Kłopotek Mieczysław, Wierzchon Slawomir T., Czerski Dariusz

Annals of Computer Science and Information Systems

2023

Vol. 35

759--767

Spectral clustering methods are claimed to possess ability to represent clusters of diverse shapes, densities etc. They constitute an approximation to graph cuts of various types (plain cuts, normalized cuts, ratio cuts). They are applicable to unweighted and weighted similarity graphs. We perform an evaluation of these capabilities for clustering tasks of increasing complexity.