Abu-Salih Bilal, Alotaibi Salihah, Al-Okaily Manaf, Aljaafari Mohammed, Almiani Muder
King Abdullah II School of Information Technology, The University of Jordan, Amman, Jordan.
Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.
Front Big Data. 2024 Nov 20;7:1469819. doi: 10.3389/fdata.2024.1469819. eCollection 2024.
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 low-dimensional 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.
品牌倡导者以在没有激励措施的情况下积极推广品牌为特征，在推动正面口碑（WOM）和影响潜在客户方面发挥着关键作用。然而，目前明显缺乏能够基于与品牌的社交互动准确识别在线倡导者的智能系统。知识图谱（KGs）提供了人类知识的结构化和事实性表示，为全面了解客户偏好以及与品牌的互动提供了潜在的解决方案。本研究提出了一个新颖的框架，该框架利用知识图谱构建和嵌入技术来准确识别品牌倡导者。通过利用知识图谱的力量，我们的框架提高了识别和理解品牌倡导者的准确性和效率，为在线领域的客户倡导动态提供了有价值的见解。此外，我们解决了社会可信度这一关键问题，它对倡导工作的影响很大。将社会可信度分析纳入我们的框架，使企业能够识别并减轻垃圾信息发送者的影响，维护真实性和客户信任。为实现这一目标，我们纳入并扩展了DSpamOnto，这是一个专门用于识别社交垃圾信息的本体，重点关注社交商务领域。此外，我们采用前沿的嵌入技术将知识图谱映射到低维向量空间，实现有效的链接预测、聚类和可视化。通过严格的评估过程，我们证明了所提出框架的有效性和性能，突出了其在助力企业培养品牌倡导者和推动有意义的客户参与策略方面的潜力。
