Search Rank Fraud De-Anonymization in Online Systems

We introduce the fraud de-anonymization problem, that goes beyond fraud detection, to unmask the human masterminds responsible for posting search rank fraud in online systems. We collect and study search rank fraud data from Upwork, and survey the capabilities and behaviors of 58 search rank fraudsters recruited from 6 crowdsourcing sites. We propose Dolos, a fraud de-anonymization system that leverages traits and behaviors extracted from these studies, to attribute detected fraud to crowdsourcing site fraudsters, thus to real identities and bank accounts. We introduce MCDense, a min-cut dense component detection algorithm to uncover groups of user accounts controlled by different fraudsters, and leverage stylometry and deep learning to attribute them to crowdsourcing site profiles. Dolos correctly identified the owners of 95% of fraudster-controlled communities, and uncovered fraudsters who promoted as many as 97.5% of fraud apps we collected from Google Play. When evaluated on 13,087 apps (820,760 reviews), which we monitored over more than 6 months, Dolos identified 1,056 apps with suspicious reviewer groups. We report orthogonal evidence of their fraud, including fraud duplicates and fraud re-posts.Comment: The 29Th ACM Conference on Hypertext and Social Media, July 201

Simple system to measure the Earth's magnetic field

Our aim in this proposal is by using the Faraday's law of induction as a simple lecture demonstration to measure the Earth's magnetic field (B). This will also enable the students to learn about how electric power is generated from the rotational motion. Obviously the idea is not original, yet it may be attractive in the sense that no sophisticated devices are used

Where Graph Topology Matters: The Robust Subgraph Problem

Robustness is a critical measure of the resilience of large networked systems, such as transportation and communication networks. Most prior works focus on the global robustness of a given graph at large, e.g., by measuring its overall vulnerability to external attacks or random failures. In this paper, we turn attention to local robustness and pose a novel problem in the lines of subgraph mining: given a large graph, how can we find its most robust local subgraph (RLS)? We define a robust subgraph as a subset of nodes with high communicability among them, and formulate the RLS-PROBLEM of finding a subgraph of given size with maximum robustness in the host graph. Our formulation is related to the recently proposed general framework for the densest subgraph problem, however differs from it substantially in that besides the number of edges in the subgraph, robustness also concerns with the placement of edges, i.e., the subgraph topology. We show that the RLS-PROBLEM is NP-hard and propose two heuristic algorithms based on top-down and bottom-up search strategies. Further, we present modifications of our algorithms to handle three practical variants of the RLS-PROBLEM. Experiments on synthetic and real-world graphs demonstrate that we find subgraphs with larger robustness than the densest subgraphs even at lower densities, suggesting that the existing approaches are not suitable for the new problem setting.Comment: 13 pages, 10 Figures, 3 Tables, to appear at SDM 2015 (9 pages only

Piecemeal Freedom: Why the Headscarf Ban Remains in Place in Turkey

The intersection of religion and politics has always been a volatile subject in Turkey. From the first years of the Republic to the present day, political leaders have had to balance the secular interests of the state with the religious beliefs of the public. Historically, it has been the religious public who has carried the brunt of this balancing act, specifically women. For decades, Muslim women wearing headscarves for religious reasons were fenced out of the public sphere because of a belief that their outwardly manifested religious beliefs threatened the secular structure of the Republic. They could not attend schools, hold office, or work in government offices if they chose to wear a headscarf. In 2013, most of these barriers were lifted through a by-law allowing headscarf-wearing women to work in most government offices. Although a step in the right direction, the by-law falls short of creating an equal space for all women as it continues to keep headscarf-wearing women out of crucial state offices, including the military, the judiciary, and the police force. With such limitations, the by-law reinforces the belief that headscarf-wearing women are not welcome in all public spaces