Extracting attributes for twitter hashtag communities

Alsulami, Ashwaq and Shao, Jianhua ORCID: https://orcid.org/0000-0001-8461-1471 2022. Extracting attributes for twitter hashtag communities. International Journal of Humanities and Social Sciences 16 (3) , pp. 171-178.

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Abstract

Various organisations often need to understand discussions on social media, such as what trending topics are and characteristics of the people engaged in the discussion. A number of approaches have been proposed to extract attributes that would characterise a discussion group. However, these approaches are largely based on supervised learning, and as such they require a large amount of labelled data. We propose an approach in this paper that does not require labelled data, but rely on lexical sources to detect meaningful attributes for online discussion groups. Our findings show an acceptable level of accuracy in detecting attributes for Twitter discussion groups

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	World Academy of Science, Engineering and Technology (WASET)
ISSN:	2070-3783
Last Modified:	22 Nov 2023 16:00
URI:	https://orca.cardiff.ac.uk/id/eprint/163550

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