Influencer marketing through social networks is becoming an important alternative to traditional ways of advertising.Various solutions have been proposed that often take advantage Emergency Lighting 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 color revitalizer 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.