Mining Twitter lists to extract brand-related associative information for celebrity endorsement

Saridakis, Charalampos, Katsikeas, Constantine S, Angelidou, Sofia ORCID: 0000-0001-9932-2859, Oikonomidou, Maria and Pratikakis, Polyvios (2023) Mining Twitter lists to extract brand-related associative information for celebrity endorsement. European Journal of Operational Research, 311 (1). pp. 316-332.

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Abstract

Twitter lists (i.e., curated collections of Twitter accounts) are user-generated and serve primarily as a tool to group other users. Grouping judgments are grounded in the implicit assumption that co-listed members share common associations. As such, Twitter lists are ideal for directly exploring associative links between brands and/or other entities. This research capitalizes on Twitter list membership data to provide a new metric indicating the similarity of users’ list membership profiles. This metric is used as a proxy for perceptions of brand–celebrity (mis)fit (i.e., the degree of congruency or similarity between the celebrity and the brand) in celebrity endorsement situations, where a celebrity's fame or social status is used to promote a brand. To validate the accuracy of the method, we compare the list similarity metric with directly elicited survey data for a test set of 62 celebrities and 64 brands, ranging across eight industry sectors. This research contributes to the extant literature of studies extracting brand-related associative information (i.e., information held in consumers’ memory that contains the meaning of a brand) from large volumes of consumer online data. This research also introduces new ways of data mining to operational research literature and provides managers with a new methodology to directly infer perceptions of brand–celebrity (mis)fit.

Item Type:	Article
Uncontrolled Keywords:	OR in marketing, Celebrity endorsement, Twitter lists, Big data, Data mining
Divisions:	Faculty of Humanities and Social Sciences > School of Management
Depositing User:	Symplectic Admin
Date Deposited:	05 May 2023 07:18
Last Modified:	24 Nov 2023 04:02
DOI:	10.1016/j.ejor.2023.05.004
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3170192