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
SIAM Journal on Discrete Mathematics
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Expertise networks in online communities: structure and algorithms
Proceedings of the 16th 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
Content feature enrichment for analyzing trust relationships in web forums
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Social media is actively utilized by extremists to spread out their ideologies. While the Internet provides a platform for any users around the world to share their opinions, some opinions in social media can be related to the national security and threatening to others. Given the large volume and exponential growing rate of messages on the social media platforms, it is impossible to analyze the messages by manual effort. An effective way to identify the threat through social media is detecting the influential users automatically. Bu identifying the influential users, we can determine the impact and the neighborhood of these users. In this work, we develop weights to incorporate message content similarity and response immediacy to measure the degree of influence between any two users on a social networking site and integrate the weights with the typical link analysis techniques. In our experiment, we investigate the impact of weights and the basic algorithms (iterative or prestige) on the user influence ranking. The experiment is conducted on the Dark Web forum provided in the ISI-KDD Challenge. The result shows that the weights make substantial impact on the ranking results, especially on the in-degree algorithm.