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Social reputation (e.g., likes, comments, shares, etc.) on YouTube is theprimary tenet to popularize channels/videos. However, the organic way toimprove social reputation is tedious, which often provokes content creators toseek the services of online blackmarkets for rapidly inflating contentreputation. Such blackmarkets act underneath a thriving collusive ecosystemcomprising core users and compromised accounts (together known as collusiveusers). Core users form the backbone of blackmarkets; thus, spotting andsuspending them may help in destabilizing the entire collusive network.Although a few studies focused on collusive user detection on Twitter,Facebook, and YouTube, none of them differentiate between core users andcompromised accounts.  We are the first to present a rigorous analysis of core users in YouTubeblackmarkets. To this end, we collect a new dataset of collusive YouTube users.We study the core-periphery structure of the underlying collusive commentingnetwork (CCN). We examine the topology of CCN to explore the behavioraldynamics of core and compromised users. We then introduce KORSE, a novelgraph-based method to automatically detect core users based only on thetopological structure of CCN. KORSE performs a weighted k-core decompositionusing our proposed metric, called Weighted Internal Core Collusive Index(WICCI). However, KORSE is infeasible to adopt in practice as it requirescomplete interactions among collusive users to construct CCN. We, therefore,propose NURSE, a deep fusion framework that only leverages user timelines(without considering the underlying CCN) to detect core blackmarket users.Experimental results show that NURSE is quite close to KORSE in detecting coreusers and outperforms nine baselines.

Weakening the Inner Strength: Spotting Core Collusive Users in YouTube Blackmarket Network