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ORIGINAL RESEARCH article

Front. Big Data
Sec. Machine Learning and Artificial Intelligence
Volume 7 - 2024 | doi: 10.3389/fdata.2024.1469819
This article is part of the Research Topic Machine Learning for CyberSecurity View all 4 articles

Credibility-based Knowledge Graph Embedding for Identifying Social Brand Advocates

Provisionally accepted
Bilal Abu-Salih Bilal Abu-Salih 1*Salihah Abu-Alotaibi Salihah Abu-Alotaibi 2Manaf Al-Okaily Manaf Al-Okaily 3Mohammed Aljaafari Mohammed Aljaafari 4Muder Almiani Muder Almiani 5
  • 1 The University of Jordan, Aljubeiha, Jordan
  • 2 College of Computer and Information Sciences, Imam Muhammad ibn Saud Islamic University, Riyadh, Saudi Arabia
  • 3 Jadara University, Irbid, Irbid, Jordan
  • 4 King Faisal University, Al-Ahsa, Eastern Province, Saudi Arabia
  • 5 Gulf University for Science and Technology, Mishref District, Kuwait

The final, formatted version of the article will be published soon.

    Brand advocates, characterized by their enthusiasm for promoting a brand without incentives, play a crucial role in driving positive word-of-mouth (WOM) and influencing potential customers. However, there is a notable lack of intelligent systems capable of accurately identifying online advocates based on their social interactions with brands. Knowledge Graphs (KGs) offer structured and factual representations of human knowledge, providing a potential solution to gain holistic insights into customer preferences and interactions with a brand. This study presents a novel framework that leverages KG construction and embedding techniques to identify brand advocates accurately. By harnessing the power of KGs, our framework enhances the accuracy and efficiency of identifying and understanding brand advocates, providing valuable insights into customer advocacy dynamics in the online realm. Moreover, we address the critical aspect of social credibility, which significantly influences the impact of advocacy efforts. Incorporating social credibility analysis into our framework allows businesses to identify and mitigate spammers, preserving authenticity and customer trust. To achieve this, we incorporate and extend DSpamOnto, a specialized ontology designed to identify social spam, with a focus on the social commerce domain. Additionally, we employ cutting-edge embedding techniques to map the KG into a lowdimensional vector space, enabling effective link prediction, clustering, and visualization. Through a rigorous evaluation process, we demonstrate the effectiveness and performance of our proposed framework, highlighting its potential to empower businesses in cultivating brand advocates and driving meaningful customer engagement strategies.

    Keywords: online customer engagement, Brand Advocates Detection, Knowledge graphs, Knowledge graph embedding, Social credibility, social media analysis

    Received: 24 Jul 2024; Accepted: 28 Oct 2024.

    Copyright: © 2024 Abu-Salih, Abu-Alotaibi, Al-Okaily, Aljaafari and Almiani. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Bilal Abu-Salih, The University of Jordan, Aljubeiha, Jordan

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.