In the Eye of the Storm: Social Media and Crisis Management

Social media, also called Web 2.0, is a generic term used to talk about applications that allow users to create, manipulate, and disseminate content as much as possible in real time. These applications allow for several possibilities that range from involvement to participation, communication, and collaboration of users. They allow everyone with minimal access to the Internet to publish, share, review, comment, and post items, such as mentions, comments, information, videos, and photos. In a crisis, social media becomes a double-edged sword. It can play an essential role during the prodromal, acute, chronic, and resolution phases of natural disasters and human-made crises. Social media can also be at the origin of the crisis or the reason for its amplification. Social media facilitates an increase of interactions between main actors at the center of a crisis. This chapter combines social media content analysis (opinion detection and sentiment analysis) with network analysis (ego network analysis) and nodes centrality assessment to critically evaluate how social media affects the crisis management process

Cryptocurrency scams: analysis and perspectives

Since the inception of Bitcoin in 2009, the market of cryptocurrencies has grown beyond the initial expectations, as witnessed by the thousands of tokenised assets available on the market, whose daily trades amount to dozens of USD billions. The pseudonymity features of these cryptocurrencies have attracted the attention of cybercriminals, who exploit them to carry out potentially untraceable scams. The wide range of cryptocurrency-based scams observed over the last ten years has fostered the research on the analysis of their effects, and the development of techniques to counter them. However, doing research in this field requires addressing several challenges: for instance, although a few data sources about cryptocurrency scams are publicly available, they often contain incomplete or misclassified data. Further, there is no standard taxonomy of scams, which leads to ambiguous and incoherent interpretations of their nature. Indeed, the unavailability of reliable datasets makes it difficult to train effective automatic classifiers that can detect and analyse cryptocurrency scams. In this paper, we perform an extensive review of the scientific literature on cryptocurrency scams, which we systematise according to a novel taxonomy. By collecting and homogenising data from different public sources, we build a uniform dataset of thousands of cryptocurrency scams.We devise an automatic tool that recognises scams and classifies them according to our taxonomy.We assess the effectiveness of our tool through standard performance metrics.We also give an in-depth analysis of the classification results, offering several insights into threat types, from their features to their connection with other types. Finally, we provide a set of guidelines that policymakers could follow to improve user protection against cryptocurrency scams

An ego network analysis of sextortionists

We consider a particular instance of user interactions in the Bitcoin network, that of interactions among wallet addresses belonging to scammers. Aggregation of multiple inputs and change addresses are common heuristics used to establish relationships among addresses and analyze transaction amounts in the Bitcoin network. We propose a flow centric approach that complements such heuristics, by studying the branching, merger and propagation of Bitcoin flows. We study a recent sextortion campaign by exploring the ego network of known offending wallet addresses. We compare and combine different existing and new heuristics, which allows us to identify (1) Bitcoin addresses of interest (including possible recurrent go-to addresses for the scammers) and (2) relevant Bitcoin flows, from scam Bitcoin addresses to a Binance exchange and to other other scam addresses, that suggest connections among prima facie disparate waves of similar scams.Accepted versio