Topic sensitive information diffusion modelling in online social networks
作者： G Gracia Michelle , P Kumaran , S Chitrakala
DOI: 10.1109/AEEICB.2016.7538262
关键词: Drawback 、 Sensitivity (control systems) 、 Popularity 、 Data science 、 Diffusion (business) 、 Information sensitivity 、 The Internet 、 Computer science 、 Artificial intelligence 、 Machine learning 、 Order (exchange) 、 Relevance (information retrieval)
摘要: Information diffusion in online social networks is gaining popularity today's world due to people's willingness connect and communicate over the internet. Social have become good places promote products campaign for worthy causes. Studying process can help make this endeavour successful. Currently existing systems are capable of providing accurate results but they all share one common drawback - not topic sensitive. In paper, an approach inspired by that taken PageRank algorithm used order introduce sensitivity currently thus giving rise proposed sensitive information model (TS-IDM). Each user's interest gauged numerically denoted a relevance score. The scores obtained along with evolutionary game theoretic diffusion. Evaluation showed accuracy 69% on average across different topics.
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