Biblio

Bitcoin, a peer-to-peer payment system and digital currency, is often involved in illicit activities such as scamming, ransomware attacks, illegal goods trading, and thievery. At the time of writing, the Bitcoin ecosystem has not yet been mapped and as such there is no estimate of the share of illicit activities. This paper provides the first estimation of the portion of cyber-criminal entities in the Bitcoin ecosystem. Our dataset consists of 854 observations categorised into 12 classes (out of which 5 are cybercrime-related) and a total of 100,000 uncategorised observations. The dataset was obtained from the data provider who applied three types of clustering of Bitcoin transactions to categorise entities: co-spend, intelligence-based, and behaviour-based. Thirteen supervised learning classifiers were then tested, of which four prevailed with a cross-validation accuracy of 77.38%, 76.47%, 78.46%, 80.76% respectively. From the top four classifiers, Bagging and Gradient Boosting classifiers were selected based on their weighted average and per class precision on the cybercrime-related categories. Both models were used to classify 100,000 uncategorised entities, showing that the share of cybercrime-related is 29.81% according to Bagging, and 10.95% according to Gradient Boosting with number of entities as the metric. With regard to the number of addresses and current coins held by this type of entities, the results are: 5.79% and 10.02% according to Bagging; and 3.16% and 1.45% according to Gradient Boosting.

Formal methods, models and tools for social big data analytics are largely limited to graph theoretical approaches such as social network analysis (SNA) informed by relational sociology. There are no other unified modeling approaches to social big data that integrate the conceptual, formal and software realms. In this paper, we first present and discuss a theory and conceptual model of social data. Second, we outline a formal model based on set theory and discuss the semantics of the formal model with a real-world social data example from Facebook. Third, we briefly present and discuss the Social Data Analytics Tool (SODATO) that realizes the conceptual model in software and provisions social data analysis based on the conceptual and formal models. Fourth and last, based on the formal model and sentiment analysis of text, we present a method for profiling of artifacts and actors and apply this technique to the data analysis of big social data collected from Facebook page of the fast fashion company, H&M.