Abstract
Spammer detection in social media has recently
received increasing attention due to the rocketing
growth of user-generated data. Despite the empirical success of existing systems, spammers may
continuously evolve over time to impersonate normal users while new types of spammers may also
emerge to combat with the current detection system, leading to the fact that a built system will gradually lose its efficacy in spotting spammers. To address this issue, grounded on the contextual bandit
model, we present a novel system for conducting
interactive spammer detection. We demonstrate our
system by showcasing the interactive learning process, which allows the detection model to keep optimizing its detection strategy through incorporating the feedback information from human experts