Modelling Requirements for Content Recommendation Systems

This paper addresses the modelling of requirements for a content Recommendation System (RS) for Online Social Networks (OSNs). On OSNs, a user switches roles constantly between content generator and content receiver. The goals and softgoals are different when the user is generating a post, as opposed as replying to a post. In other words, the user is generating instances of different entities, depending on the role she has: a generator generates instances of a "post", while the receiver generates instances of a "reply". Therefore, we believe that when addressing Requirements Engineering (RE) for RS, it is necessary to distinguish these roles clearly. We aim to model an essential dynamic on OSN, namely that when a user creates (posts) content, other users can ignore that content, or themselves start generating new content in reply, or react to the initial posting. This dynamic is key to designing OSNs, because it influences how active users are, and how attractive the OSN is for existing, and to new users. We apply a well-known Goal Oriented RE (GORE) technique, namely i-star, and show that this language fails to capture this dynamic, and thus cannot be used alone to model the problem domain. Hence, in order to represent this dynamic, its relationships to other OSNs' requirements, and to capture all relevant information, we suggest using another modelling language, namely Petri Nets, on top of i-star for the modelling of the problem domain. We use Petri Nets because it is a tool that is used to simulate the dynamic and concurrent activities of a system and can be used by both practitioners and theoreticians.Comment: 28 pages, 7 figure

Machine Learning on Blockchain Data: A Systematic Mapping Study

Context: Blockchain technology has drawn growing attention in the literature and in practice. Blockchain technology generates considerable amounts of data and has thus been a topic of interest for Machine Learning (ML). Objective: The objective of this paper is to provide a comprehensive review of the state of the art on machine learning applied to blockchain data. This work aims to systematically identify, analyze, and classify the literature on ML applied to blockchain data. This will allow us to discover the fields where more effort should be placed in future research. Method: A systematic mapping study has been conducted to identify the relevant literature. Ultimately, 159 articles were selected and classified according to various dimensions, specifically, the domain use case, the blockchain, the data, and the machine learning models. Results: The majority of the papers (49.7%) fall within the Anomaly use case. Bitcoin (47.2%) was the blockchain that drew the most attention. A dataset consisting of more than 1.000.000 data points was used by 31.4% of the papers. And Classification (46.5%) was the ML task most applied to blockchain data. Conclusion: The results confirm that ML applied to blockchain data is a relevant and a growing topic of interest both in the literature and in practice. Nevertheless, some open challenges and gaps remain, which can lead to future research directions. Specifically, we identify novel machine learning algorithms, the lack of a standardization framework, blockchain scalability issues and cross-chain interactions as areas worth exploring in the future

Assessing the impact of network factors and Twitter data on Ethereum's popularity

In March 2021, we witnessed a surge in Bitcoin price. The cause seemed to be a tweet by Elon Musk. Are other blockchains as sensitive to social media as Bitcoin? And more precisely, could Ethereum's popularity be explained using social media data?This work aims to explore the determinants of Ethereum's popularity. We use both data from Etherscan to retrieve the relevant historic Ethereum factors and Twitter data. Our sample consists of data ranging from 2015 to 2022. We use Ordinary Least Squares to assess the relationship between these factors (Ethereum characteristics and Twitter data) and Ethereum's popularity.Our findings show that Ethereum's popularity—translated here by the number of daily new addresses—is related to the following elements: the Ether (ETH) price, the transaction fees, and the polarity of tweets related to Ethereum.The results could have multiple practical implications for both researchers and practitioners. First of all, we believe that it will enable readers to better understand the technology of Ethereum and its stake. Secondly, it will help the community identify pointers for anticipating or explaining the popularity of existing or future platforms. And finally, the results could help in understanding the factors facilitating the design of future platforms