The Tales of Alphanumerical Symbols in Media: The Case of Bitcoin

Bitcoin, a decentralized cryptocurrency, has not only given rise to a wave of digital innovations but also stirred up considerable controversy. Some have hailed it as the most significant innovation since the Internet, while others have dismissed it as a Ponzi scheme that should be abandoned and forbidden. Regardless of these varying views, this is an innovation in need of scrutiny. In this paper we present a metastory of Bitcoin, based on an interpretative study of 737 news articles between 2011–2019. Through our analysis, we identified five narratives, including The Dark Side, The Bright Side, The Tulip Mania, The Idea, and The Normality. Our analysis demonstrates the interpretive flexibility of technology as influenced by ideologies, and we construct a theoretical model demonstrating media’s role as constructor and conduit. The metastory provides an institutional look at the broader interpretations of digital innovations as well as the multifaceted nature of digital innovations and how their interpretation evolve over time.

Improving Ponzi Scheme Contract Detection Using Multi-Channel TextCNN and Transformer

With the development of blockchain technologies, many Ponzi schemes disguise themselves under the veil of smart contracts. The Ponzi scheme contracts cause serious financial losses, which has a bad effect on the blockchain. Existing Ponzi scheme contract detection studies have mainly focused on extracting hand-crafted features and training a machine learning classifier to detect Ponzi scheme contracts. However, the hand-crafted features cannot capture the structural and semantic feature of the source code. Therefore, in this study, we propose a Ponzi scheme contract detection method called MTCformer (Multi-channel Text Convolutional Neural Networks and Transofrmer). In order to reserve the structural information of the source code, the MTCformer first converts the Abstract Syntax Tree (AST) of the smart contract code to the specially formatted code token sequence via the Structure-Based Traversal (SBT) method. Then, the MTCformer uses multi-channel TextCNN (Text Convolutional Neural Networks) to learn local structural and semantic features from the code token sequence. Next, the MTCformer employs the Transformer to capture the long-range dependencies of code tokens. Finally, a fully connected neural network with a cost-sensitive loss function in the MTCformer is used for classification. The experimental results show that the MTCformer is superior to the state-of-the-art methods and its variants in Ponzi scheme contract detection.

A Socio-Historical Perspective on Ponzi Schemes and Development in Nigeria: The Role of the Christian Church

This paper is a study on Ponzi schemes, development and the Christian church in Nigeria. It traced the emergence of Ponzi schemes in Nigeria. The paper considered the practices of Mavrodi Mondial Movement (MMM), being one of the strongest Ponzi schemes in Nigeria. It assessed the impact of this Ponzi scheme on development in the country. It also looked into the role played by the Christian Church during the period of the scheme’s existence in the country. The paper, however concluded that, despite the people involved in the scheme being interested in supposedly helping people (including those in the scheme and the less privileged), it is contrary to the ethos of Christianity that touches on labour and its corresponding success. The data collected for the study were analysed using socio-historical approach.

Situational Criminogenic: The Financial Crime’s Collaborative Factors in Indonesia

Financial crimes in Indonesia from 2014-2018 were classified as quite dynamic with a total of 241,367 cases. In 2018 the legal unit area of Polda Metro Jaya had the highest number of cases of 5,526 cases of financial crimes. This study seeks to examine the determinant aspects of financial crime in Indonesia. I used the illustration of the case of First Travel and the Koperasi Simpan Pinjam (KSP) Pandawa that occurred in Indonesia with a total loss of up to IDR 1 trillion. Discussions in this paper begin from the point of view of white collar crime that elaborated with criminaloid and organizational criminogenic aspects. This study uses a grounded theory method through in-depth interviews of actors. The result is that on the criminaloid aspect, the perpetrators have a tendency to easily confess, have certain social and cultural status, has moral sensitivity and intelligence, and has skills, but hesitates in acting. Meanwhile, in the organizational criminogenic aspect, it was found that the perpetrators were in an environment with profit-oriented ambitions, had certain business perceptions, had a loyal attitude towards their group and their human resources tended to be homogeneous. The results of this study found that a supportive situation is needed in financial crime based on the illustrations of the cases used. Situational criminogenic aspects in research in the form of business that utilize religious sentiments, use a cooperative system and manage funds with a Ponzi scheme. This research will enrich criminology studies, especially in the field of white collar crime. Other than that, hopefully can be useful in the formulation of policies for stakeholders.

SADPonzi: Detecting and Characterizing Ponzi Schemes in Ethereum Smart Contracts

Ponzi schemes are financial scams that lure users under the promise of high profits. With the prosperity of Bitcoin and blockchain technologies, there has been growing anecdotal evidence that this classic fraud has emerged in the blockchain ecosystem. Existing studies have proposed machine-learning based approaches for detecting Ponzi schemes, i.e., either based on the operation codes (opcodes) of the smart contract binaries or the transaction patterns of addresses. However, state-of-the-art approaches face several major limitations, including lacking interpretability and high false positive rates. Moreover, machine-learning based methods are susceptible to evasion techniques, and transaction-based techniques do not work on smart contracts that have a small number of transactions. These limitations render existing methods for detecting Ponzi schemes ineffective. In this paper, we propose SADPonzi, a semantic-aware detection approach for identifying Ponzi schemes in Ethereum smart contracts. Specifically, by strictly following the definition of Ponzi schemes, we propose a heuristic-guided symbolic execution technique to first generate the semantic information for each feasible path in smart contracts and then identify investor-related transfer behaviors and the distribution strategies adopted. Experimental result on a well-labelled benchmark suggests that SADPonzi can achieve 100% precision and recall, outperforming all existing machine-learning based techniques. We further apply SADPonzi to all 3.4 million smart contracts deployed by EOAs in Ethereum and identify 835 Ponzi scheme contracts, with over 17 million US Dollars invested by victims. Our observations confirm the urgency of identifying and mitigating Ponzi schemes in the blockchain ecosystem.

Situational Criminogenic: The Financial Crime’s Collaborative Factors in Indonesia

Financial crimes in Indonesia from 2014-2018 were classified as quite dynamic with a total of 241,367 cases. In 2018 the legal unit area of Polda Metro Jaya had the highest number of cases of 5,526 cases of financial crimes. This study seeks to examine the determinant aspects of financial crime in Indonesia. I used the illustration of the case of First Travel and the Koperasi Simpan Pinjam (KSP) Pandawa that occurred in Indonesia with a total loss of up to IDR 1 trillion. Discussions in this paper begin from the point of view of white collar crime that elaborated with criminaloid and organizational criminogenic aspects. This study uses a grounded theory method through in-depth interviews of actors. The result is that on the criminaloid aspect, the perpetrators have a tendency to easily confess, have certain social and cultural status, has moral sensitivity and intelligence, and has skills, but hesitates in acting. Meanwhile, in the organizational criminogenic aspect, it was found that the perpetrators were in an environment with profit-oriented ambitions, had certain business perceptions, had a loyal attitude towards their group and their human resources tended to be homogeneous. The results of this study found that a supportive situation is needed in financial crime based on the illustrations of the cases used. Situational criminogenic aspects in research in the form of business that utilize religious sentiments, use a cooperative system and manage funds with a Ponzi scheme. This research will enrich criminology studies, especially in the field of white collar crime. Other than that, hopefully can be useful in the formulation of policies for stakeholders.

Gender and age-based investor affinities in a Ponzi scheme

Utilizing a police dataset of a fundraising Ponzi scheme in China, we establish referrer-investor links and examine how investor affinity in terms of gender and age affects the way the scheme spreads and the way investors suffer losses. We find that female or older investors are more susceptible to investor affinity. Specifically, female or older investors are more likely to be referred into the scheme by female or older investors. Female or older investors tend to occupy lower layers in the investor hierarchy of the scheme and they are more likely to occupy lower layers if they are referred into the scheme by female or older investors. Consequently, female or older investors suffer more losses if they are referred into the scheme by female or older investors. We conclude that gender and age-based investor affinities are especially pronounced among female or older investors in a Ponzi scheme.