Real-time Influencer Detection In Twitter Using A Hybrid Approach.
S. Siji Rani, Sangeeth Sajan Baby
Abstract
Social media is attaining popularity day by day across domains. Approximately about 40% of the world's population uses social media. Automatically these people look the influencers in social media to guide them with their decision-making in a day-to-day life. An influencer in social media is someone who can influence others. Finding the best influencer as a seed node among social media users is relevant to research. This paper proposes a methodology that can get the most suitable influencer list based on context analysis. A contextual analysis analyzes a text in different media that helps us assess that text within the context. The hybrid way of using the graph networks and the machine learning algorithms like LSTM and Naive Bayes together as suggested in this article helps us assess that text and find the influencer significantly better than the existing approaches. We prove that the hybrid model can produce more accurate results in such a way that we only get the users who can positively influencer other users. Based on our result, we are sure that this hybrid model can produce more accurate results for funding the best set of influencers. Along with that, the combined use of LSTM and Naive Bayes can produce better results.