Joint theme and event based rating model for identifying relevant influencers on Twitter: COVID-19 case study

Srour, Ali, Ould-Slimane, Hakima, Mourad, Azzam, Harmanani, Haidar and Jenainati, Cathia (2022) Joint theme and event based rating model for identifying relevant influencers on Twitter: COVID-19 case study. Online Social Networks and Media, 31. Art 100226. doi:10.1016/j.osnem.2022.100226

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Abstract

The continuous proliferation of social media platforms and the exponential increase in users’ engagement are impacting social behavior and leading to various challenges, including the detection and identification of key influencers. In fact the opinions of these influencers are at the core of decision-making strategies, and are leading trends on the virtual social media landscape. Moreover, influencers might play a crucial role when it comes to misinformation and conspiracy during sensitive, controversial and trending events. However, due to the dynamic and unrestricted nature of social media, and diversity of targeted topics and audiences, identifying and ranking key influencers that are impactful, credible, and knowledgeable about their specialist topic or event remains an evolving and open research paradigm. In this paper, we address the aforementioned problem by proposing a novel influence rating and ranking scheme to identify key and highly influential users for a certain event over Twitter using a mixed theme/event based approach while considering historical data and profile reputation. We further apply our approach to a global pandemic case study, the novel Coronavirus, and conduct performance analysis. The presented experimental results and theoretical analysis explore the relevance of our proposed scheme for identifying and ranking reputable and theme/event related influencers.

Item Type:	Article
Article Type:	Article
Uncontrolled Keywords:	Social Network Analysis; Twitter; Influence rating; Influencers; Reputation; Social Listening; Computational Social Science; User impact; Big data; Data science; COVID-19; Infodemic; Natural Language Processing
Subjects:	H Social Sciences > HF Commerce > HF5001 Business Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software > QA76.9 Other topics > QA76.9.B45 Big data
Divisions:	Schools and Research Institutes > School of Business, Computing and Social Sciences
Research Priority Areas:	Applied Business & Technology
Depositing User:	Rhiannon Goodland
Date Deposited:	24 Oct 2023 14:01
Last Modified:	26 Jul 2024 04:15
URI:	https://eprints.glos.ac.uk/id/eprint/13283

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