Modeling Implicit User Relations with Information Propagation Graph for User Influence Prediction

Author(s):  
Jenq-Haur Wang ◽  
Po-Hung Lin
Author(s):  
Meeyoung Cha ◽  
Fabrício Benevenuto ◽  
Saptarshi Ghosh ◽  
Krishna Gummadi

Social media and blogging services have become extremely popular. Every day hundreds of millions of users share random thoughts, gossip, news, and thoughts on notable social issues. Users interact by following each other’s updates and passing along interesting pieces of information to their friends. Information therefore can diffuse widely and quickly through social links. Information propagation in networks such as Twitter and Facebook is unique, in that traditional media sources and word-of-mouth propagation coexist. The availability of digitally logged propagation events in social media helps one better understand how a wide range of factors that are essential in communication, such as user influence, tie strength, repeated exposures, mass media, and agenda setting, come into play in the way people generate and consume information in modern society. This chapter reviews the roles different types of users of social media play in information propagation as well as the resulting propagation patterns. It also discusses specific examples, including the spread of social conventions and identifying topic experts in social media, in an effort to bring about better understanding of the characteristics of propagation phenomena in large social networks.


2021 ◽  
Vol 11 (6) ◽  
pp. 2530
Author(s):  
Minsoo Lee ◽  
Soyeon Oh

Over the past few years, the number of users of social network services has been exponentially increasing and it is now a natural source of data that can be used by recommendation systems to provide important services to humans by analyzing applicable data and providing personalized information to users. In this paper, we propose an information recommendation technique that enables smart recommendations based on two specific types of analysis on user behaviors, such as the user influence and user activity. The components to measure the user influence and user activity are identified. The accuracy of the information recommendation is verified using Yelp data and shows significantly promising results that could create smarter information recommendation systems.


Author(s):  
Xiang LIU ◽  
Yan JIA ◽  
Rong JIANG ◽  
Yong QUAN

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