Identifying Key Opinion Leaders in Social Media via Modality-Consistent Harmonized Discriminant Embedding

Yang Liu*, Zhonglei Gu, Tobey H. Ko, Jiming Liu

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

12 Citations (Scopus)


The digital age has empowered brands with new and more effective targeted marketing tools in the form of key opinion leaders (KOLs). Because of the KOLs' unique capability to draw specific types of audience and cultivate long-term relationship with them, correctly identifying the most suitable KOLs within a social network is of great importance, and sometimes could govern the success or failure of a brand's online marketing campaigns. However, given the high dimensionality of social media data, conducting effective KOL identification by means of data mining is especially challenging. Owing to the generally multiple modalities of the user profiles and user-generated content (UGC) over the social networks, we can approach the KOL identification process as a multimodal learning task, with KOLs as a rare yet far more important class over non-KOLs in our consideration. In this regard, learning the compact and informative representation from the high-dimensional multimodal space is crucial in KOL identification. To address this challenging problem, in this paper, we propose a novel subspace learning algorithm dubbed modality-consistent harmonized discriminant embedding (MCHDE) to uncover the low-dimensional discriminative representation from the social media data for identifying KOLs. Specifically, MCHDE aims to find a common subspace for multiple modalities, in which the local geometric structure, the harmonized discriminant information, and the modality consistency of the dataset could be preserved simultaneously. The above objective is then formulated as a generalized eigendecomposition problem and the closed-form solution is obtained. Experiments on both synthetic example and a real-world KOL dataset validate the effectiveness of the proposed method.

Original languageEnglish
Pages (from-to)717-728
Number of pages12
JournalIEEE Transactions on Cybernetics
Issue number2
Early online date9 Oct 2018
Publication statusPublished - Feb 2020

Scopus Subject Areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

User-Defined Keywords

  • Clustering algorithms
  • Computational modeling
  • Correlation
  • Harmonized discrimination preserving
  • key opinion leader (KOL) identification
  • Learning systems
  • local geometry preserving
  • Manuals
  • modality consistency preserving
  • modality-consistent harmonized discriminant embedding (MCHDE)
  • multimodal learning
  • social media
  • Social network services
  • subspace learning
  • Task analysis


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