Typification of Impersonated Accounts on Instagram Fake accounts and Impersonators on Online Social Networks such as Instagram are turning difficulties for society. This has attended to an increasing interest in detecting fake profiles and investigating their behaviours. Questions like who are impersonators? what are their characteristics? and are they bots? will arise. To answer, we begin this research by collecting data from three important communities on Instagram including xe2x80x9cPoliticianxe2x80x9d, xe2x80x9cNews agencyxe2x80x9d, and xe2x80x9cSports starxe2x80x9d. Inside each community, four verified top accounts are picked. Based on the users who reacted to their published posts, we detect 4K impersonators 1. Then we employed wellknown clustering methods to distribute impersonators into separated clusters to observe obscure behaviours and unusual profile characteristics. We also studied the crossgroup analysis of clusters inside each community to explore engagements. Finally, we conclude the study by providing a complete investigation of the botlike cluster.

SliceNDice: Mining Suspicious MultiAttribute Entity Groups with MultiView Graphs Given the reach of web platforms, bad actors have considerable incentives to manipulate and defraud users at the expense of platform integrity. This has spurred research in numerous suspicious behavior detection tasks, including detection of sybil accounts, false information, and payment scamsfraud. In this paper, we draw the insight that many such initiatives can be tackled in a common framework by posing a detection task which seeks to find groups of entities which share too many properties with one another across multiple attributes (sybil accounts created at the same time and location, propaganda spreaders broadcasting articles with the same rhetoric and with similar reshares, etc.) Our work makes four core contributions: Firstly, we posit a novel formulation of this task as a multiview graph mining problem, in which distinct views reflect distinct attribute similarities across entities, and contextual similarity and attribute importance are respected. Secondly, we propose a novel suspiciousness metric for scoring entity groups given the abnormality of their synchronicity across multiple views, which obeys intuitive desiderata that existing metrics do not. Finally, we propose the SliceNDice algorithm which enables efficient extraction of highly suspicious entity groups, and demonstrate its practicality in production, in terms of strong detection performance and discoveries on Snapchatu0027s large advertiser ecosystem (89% precision and numerous discoveries of real fraud rings), marked outperformance of baselines (over 97% precisionrecall in simulated settings) and linear scalability.