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Topic detection is the task of determining and tracking hot topics in socialmedia. Twitter is arguably the most popular platform for people to share theirideas with others about different issues. One such prevalent issue is theCOVID-19 pandemic. Detecting and tracking topics on these kinds of issues wouldhelp governments and healthcare companies deal with this phenomenon. In thispaper, we propose a novel, multi-agent, communicative clustering approach,so-called ComStreamClust for clustering sub-topics inside a broader topic,e.g., COVID-19. The proposed approach is parallelizable, and can simultaneouslyhandle several data-point. The LaBSE sentence embedding is used to measure thesemantic similarity between two tweets. ComStreamClust has been evaluated ontwo datasets: the COVID-19 and the FA CUP. The results obtained fromComStreamClust approve the effectiveness of the proposed approach when comparedto existing methods.

ComStreamClust: a communicative multi-agent approach to text clustering in streaming data