A Scalable Data Mining Model for Social Media Influencer Identification

More, Jyoti Sunil; Lingam, Chelpa

doi:10.1007/978-981-10-3433-6_75

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…This approach is often used to calculate general influencer score and raking of influencers (Arora et al, 2019;More and Lingam, 2016). However, in order to specify categorical influencers, we have to determine the topics that influencers discuss in their contents.…”

Section: Influencer Detection On Social Mediamentioning

confidence: 99%

CID: Categorical Influencer Detection on microtext-based social media

Quan

Mai

Tran

2020

OIR

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PurposeThis paper proposes an approach to identify categorical influencers (i.e. influencers is the person who is active in the targeted categories) in social media channels. Categorical influencers are important for media marketing but to automatically detect them remains a challenge.Design/methodology/approachWe deployed the emerging deep learning approaches. Precisely, we used word embedding to encode semantic information of words occurring in the common microtext of social media and used variational autoencoder (VAE) to approximate the topic modeling process, through which the active categories of influencers are automatically detected. We developed a system known as Categorical Influencer Detection (CID) to realize those ideas.FindingsThe approach of using VAE to simulate the Latent Dirichlet Allocation (LDA) process can effectively handle the task of topic modeling on the vast dataset of microtext on social media channels.Research limitations/implicationsThis work has two major contributions. The first one is the detection of topics on microtexts using deep learning approach. The second is the identification of categorical influencers in social media.Practical implicationsThis work can help brands to do digital marketing on social media effectively by approaching appropriate influencers. A real case study is given to illustrate it.Originality/valueIn this paper, we discuss an approach to automatically identify the active categories of influencers by performing topic detection from the microtext related to the influencers in social media channels. To do so, we use deep learning to approximate the topic modeling process of the conventional approaches (such as LDA).

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Section: Influencer Detection On Social Mediamentioning

confidence: 99%