Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Identification of influencers - Measuring influence in customer networks
Decision Support Systems
New Product Diffusion with Influentials and Imitators
Marketing Science
Social influence and the diffusion of user-created content
Proceedings of the 10th ACM conference on Electronic commerce
Social and Economic Networks
Power-Law Distributions in Empirical Data
SIAM Review
Impact of social neighborhood on diffusion of innovation S-curve
Decision Support Systems
A theoretical model of intentional social action in online social networks
Decision Support Systems
Networks: An Introduction
The influence of collaborative technology knowledge on advice network structures
Decision Support Systems
Crawling Facebook for social network analysis purposes
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
Optimal decision making for online referral marketing
Decision Support Systems
Bayesian Reasoning and Machine Learning
Bayesian Reasoning and Machine Learning
A diffusion mechanism for social advertising over microblogs
Decision Support Systems
Vehicle defect discovery from social media
Decision Support Systems
Whose and what chatter matters? The effect of tweets on movie sales
Decision Support Systems
What's buzzing in the blizzard of buzz? Automotive component isolation in social media postings
Decision Support Systems
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The term 'viral' is used to describe a phenomenon that tends to be shared by those who encounter it. This paper considers the act of responding positively to a phenomenon by sharing it with others, something exemplified by the online social media acts of choosing to 'like' on Facebook, 'retweet' on Twitter, or by a similar mechanism on websites such as LinkedIn, Flickr or Pinterest. Using a threshold model of influence, simulations are run on four network structures where a critical mass chooses to share a phenomenon that eventually either goes viral or does not. The data collected are examined to determine whether an individual node can make an accurate prediction about the state of the entire network just from information on the behavior of their neighbors. The intention is to study what it is in terms of network structure that makes an individual good at sensing the zeitgeist, or 'spirit of the age'. Findings show that those best placed to predict are 'important' as measured by network centrality, and members of numerous communities. The characteristics of the critical mass are important in determining the spread of a phenomenon and it is possible for an individual node to predict an outcome as well as an observer who has access to the state of every node in the network. Potential applications might be found in monitoring the success of marketing campaigns, or in organizations wishing to keep abreast of current trends in a situation where data on network structure is available but data on the activity of network members is limited.