Discovering shared interests using graph analysis
Communications of the ACM - Special issue on internetworking
Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Expertise identification using email communications
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Expertise networks in online communities: structure and algorithms
Proceedings of the 16th international conference on World Wide Web
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering authorities in question answer communities by using link analysis
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Yes, there is a correlation: - from social networks to personal behavior on the web
Proceedings of the 17th international conference on World Wide Web
Influence and correlation in social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Feedback effects between similarity and social influence in online communities
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering leaders from community actions
Proceedings of the 17th ACM conference on Information and knowledge management
Learning to recognize reliable users and content in social media with coupled mutual reinforcement
Proceedings of the 18th international conference on World wide web
Community gravity: measuring bidirectional effects by trust and rating on online social networks
Proceedings of the 18th international conference on World wide web
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Social influence analysis in large-scale networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A study of inter-annotator agreement for opinion retrieval
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Learning influence probabilities in social networks
Proceedings of the third ACM international conference on Web search and data mining
Helping you to help me: exploring supportive interaction in online health community
Proceedings of the 73rd ASIS&T Annual Meeting on Navigating Streams in an Information Ecosystem - Volume 47
Approximate solutions for the influence maximization problem in a social network
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Intelligent systems and technology for integrative and predictive medicine: An ACP approach
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on agent communication, trust in multiagent systems, intelligent tutoring and coaching systems
| Hi-index | 0.00 |
Due to the revolutionary development of Web 2.0 technology, individual users have become major contributors of Web content in online social media. In light of the growing activities, how to measure a user’s influence to other users in online social media becomes increasingly important. This research need is urgent especially in the online healthcare community since positive influence can be beneficial while negative influence may cause-negative impact on other users of the same community. In this article, a research framework was proposed to study user influence within the online healthcare community. We proposed a new approach to incorporate users’ reply relationship, conversation content and response immediacy which capture both explicit and implicit interaction between users to identify influential users of online healthcare community. A weighted social network is developed to represent the influence between users. We tested our proposed techniques thoroughly on two medical support forums. Two algorithms UserRank and Weighted in-degree are benchmarked with PageRank and in-degree. Experiment results demonstrated the validity and effectiveness of our proposed approaches.