Inferring Web communities from link topology
Proceedings of the ninth ACM conference on Hypertext and hypermedia : links, objects, time and space---structure in hypermedia systems: links, objects, time and space---structure in hypermedia systems
Information diffusion through blogspace
ACM SIGKDD Explorations Newsletter
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Why we twitter: understanding microblogging usage and communities
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Influence and correlation in social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
Inferring Networks of Diffusion and Influence
ACM Transactions on Knowledge Discovery from Data (TKDD)
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Twitter has become a key social media for sharing information, not only for casual conversations but also for business and technologies. As the Twitter community continues to grow, an intriguing question is to determine how to obtain most valuable information the earliest by following fewest Tweeters or Tweets. This multi-criteria optimization problem exhibits similar features as in the information cascade problem for blogs. This work revises an information cascade outbreak detection algorithm to find critical Twitter accounts that disseminate the most cyber vulnerabilities the earliest. Three award functions are defined to evaluate every account's contribution per topic from three aspects: timeliness, originality and influence. Critical users are selected according to their total contribution on a specific security category. Experiments were conducted using Tweets containing CVE information over a five-week period, to compare the proposed algorithm with account selections based on the number of followers and based on the PageRank algorithm. The results show that with the same number of users and tweets, our algorithm outperforms in both information coverage and timeliness.