The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Proceedings of the 11th international conference on World Wide Web
On an equivalence between PLSI and LDA
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
The Journal of Machine Learning Research
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Graph mining: Laws, generators, and algorithms
ACM Computing Surveys (CSUR)
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Information Retrieval
Introduction to Information Retrieval
Efficient Jump Ahead for F2-Linear Random Number Generators
INFORMS Journal on Computing
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
TwitterRank: finding topic-sensitive influential twitterers
Proceedings of the third ACM international conference on Web search and data mining
Distributed Algorithms for Topic Models
The Journal of Machine Learning Research
Nephele/PACTs: a programming model and execution framework for web-scale analytical processing
Proceedings of the 1st ACM symposium on Cloud computing
Pregel: a system for large-scale graph processing
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
HaLoop: efficient iterative data processing on large clusters
Proceedings of the VLDB Endowment
An architecture for parallel topic models
Proceedings of the VLDB Endowment
Identifying topical authorities in microblogs
Proceedings of the fourth ACM international conference on Web search and data mining
Part-of-speech tagging for Twitter: annotation, features, and experiments
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Hyracks: A flexible and extensible foundation for data-intensive computing
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Scalable inference in latent variable models
Proceedings of the fifth ACM international conference on Web search and data mining
Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing
NSDI'12 Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation
Cognos: crowdsourcing search for topic experts in microblogs
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Topic-Aware Social Influence Propagation Models
ICDM '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining
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Social influence analysis on microblog networks, such as Twitter, has been playing a crucial role in online advertising and brand management. While most previous influence analysis schemes rely only on the links between users to find key influencers, they omit the important text content created by the users. As a result, there is no way to differentiate the social influence in different aspects of life (topics). Although a few prior works do support topic-specific influence analysis, they either separate the analysis of content from the analysis of network structure, or assume that content is the only cause of links, which is clearly an inappropriate assumption for microblog networks. To address the limitations of the previous approaches, we propose a novel Followship-LDA (FLDA) model, which integrates both content topic discovery and social influence analysis in the same generative process. This model properly captures the content-related and content-independent reasons why a user follows another in a microblog network. We demonstrate that FLDA produces results with significantly better precision than existing approaches. Furthermore, we propose a distributed Gibbs sampling algorithm for FLDA, and demonstrate that it provides excellent scalability on large clusters. Finally, we incorporate the FLDA model in a general search framework for topic-specific influencers. A user freely expresses his/her interest by typing a few keywords, the search framework will return a ranked list of key influencers that satisfy the user's interest.