Affiliation:
1. Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
Abstract
Influencer marketing through social networks is becoming an important alternative to traditional ways of advertising. Various solutions have been proposed that often take advantage of graph-based approaches to discover influencers in social networks. This paper designs a new method for the discovery of influential users in Instagram, by focusing on user-generated posts as an alternative source of information, to potentially augment the existing solutions based on network topology or connections. The text associated with each Instagram post potentially consists of a set of hashtags and a descriptive caption. Various word embedding methods such as Co-occurrence and fastText are examined to represent captions and hashtags. These representations are combined within a support vector machines framework to distinguish influential posts from non-influential ones. Extensive experiments show that the text data can play a significant role in identifying influential posts, and further demonstrate the strength of the proposed method for discovering influencers on Instagram.
Subject
Applied Mathematics,Control and Optimization,Instrumentation
Cited by
16 articles.
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