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.

Integrating media elements of various medium, multimedia is capable of expressing complex information in a neat and compact way. Early studies have linked different sensory presentation in multimedia with the perception of human-like concepts. Yet, the richness of information in multimedia makes understanding and predicting user perceptions in multimedia content a challenging task both to the machine and the human mind. This paper presents a novel multi-task feature extraction method for accurate prediction of user perceptions in multimedia content. Differentiating from the conventional feature extraction algorithms which focus on perfecting a single task, the proposed model recognizes the commonality between different perceptions (e.g., interestingness and emotional impact), and attempts to jointly optimize the performance of all the tasks through uncovered commonality features. Using both a media interestingness dataset and a media emotion dataset for user perception prediction tasks, the proposed model attempts to simultaneously characterize the individualities of each task and capture the commonalities shared by both tasks, and achieves better accuracy in predictions than other competing algorithms on real-world datasets of two related tasks: MediaEval 2017 Predicting Media Interestingness Task and MediaEval 2017 Emotional Impact of Movies Task.

Following the rising prominence of online social networks, we observe an emerging trend for brands to adopt influencer marketing, embracing key opinion leaders (KOLs) to reach potential customers (PCs) online. Owing to the growing strategic importance of these brand key assets, this paper presents a novel feature extraction method named Multi-modal Asset-aware Projection (M2A2P) to learn a discriminative subspace from the high-dimensional multi-modal social media data for effective brand key asset discovery. By formulating a new asset-aware discriminative information preserving criterion, M2A2P differentiates with the existing multi-model feature extraction algorithms in two pivotal aspects: 1) We consider brand's highly imbalanced class interest steering towards the KOLs and PCs over the irrelevant users; 2) We consider a common observation that a user is not exclusive to a single class (e.g. a KOL can also be a PC). Experiments on a real-world apparel brand key asset dataset validate the effectiveness of the proposed method.

As social media grown to become an integral part of many people's daily life, brands are quick to launch targeted social media marketing campaign to acquire new potential customers online. To facilitate the potential customer discovery process, a costly and labor intensive manual selection process is done to build a brand portfolio consisting of multimedia data relevant to the brand. To automate this process in a cost-effective way, in this paper, we propose a novel Multi-Modal Distance Metric Learning (M2DML) method, which learns a data-dependent similarity metric from multi-modal media data, aiming at assisting the brands to retrieve appropriate media data from social networks for potential customer discovery. To comprehensively model the supervised information of multi-modal data, M2DML aims to learn both the intra-modality and inter-modality distance metrics simultaneously. To further explore the unsupervised information of the dataset, M2DML aims to preserve the manifold structure of the multi-modal data. The proposed method is then formulated as a standard eigen-decomposition problem and the closed form solution is efficiently computed. Experiments on a standard multi-modal media dataset and a self-collected dataset validate the effectiveness of the proposed method.

Media interestingness prediction plays an important role in many real-world applications and attracts much research attention recently. In this paper, we aim to investigate this problem from the perspective of supervised feature extraction. Specifically, we design a novel algorithm dubbed Multi-view Manifold Learning (M) to uncover the latent factors that are capable of distinguishing interesting media data from non-interesting ones. By modelling both geometry preserving criterion and discrimination maximization criterion in a unified framework, M2L learns a common subspace for data from multiple views. The analytical solution of M2L is obtained by solving a generalized eigen-decomposition problem. Experiments on the Predicting Media Interestingness Dataset validate the effectiveness of the proposed method.

Accurate and effective discovery of a brand's key assets, namely, Key Opinion Leaders (KOLs) and potential customers, plays an essential role in marketing campaigns. In a massive online social network, brands are challenged with identifying a small portion of key assets over an enormous volume of irrelevant users, making the problem a highly imbalanced one. Moreover, having to deal with social media data that are usually high-dimensional, the task of brand key asset discovery can be immensely expensive yet inaccurate if the information are not processed efficiently to extract representative features from the original space prior to the learning process. To address the above issues, we propose a novel method dubbed Cluster-wise Biased Discriminant Projection (CBDP) to uncover the compact and informative features from users' data for brand key asset discovery. CBDP conducts a two-layer learning procedure. In the first layer, a Discriminant Clustering (DC) scheme is developed to partition the original dataset into clusters with maximum discriminant capacity. In the second layer, a Biased Discriminant Projection (BDP) algorithm is proposed and performed on each cluster to map the high-dimensional data to the low-dimensional subspace, where the discriminant information of classes with high importance/preference is preserved. A unified mapping function of CBDP is finally established by integrating these two layers. Experiments on both synthetic examples and a real-world brand key asset dataset validate the effectiveness of the proposed method.