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An analysis of user influence ranking algorithms on Dark Web forums

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.