With the advent of rapid development of wearable technology and mobile computing, huge amount of personal health-related data is being generated and accumulated on continuous basis at every moment. These personal datasets contain valuable information and they belong to and asset of the individual users, hence should be owned and controlled by themselves. Currently most of such datasets are stored and controlled by different service providers and this centralised data storage brings challenges of data security and hinders the data sharing. These personal health data are valuable resources for healthcare research and commercial projects. In this research work, we propose a conceptual design for sharing personal continuousdynamic health data using blockchain technology supplemented by cloud storage to share the health-related information in a secure and transparent manner. Besides, we also introduce a data quality inspection module based on machine learning techniques to have control over data quality. The primary goal of the proposed system is to enable users to own, control and share their personal health data securely, in a General Data Protection Regulation (GDPR) compliant way to get benefit from their personal datasets. It also provides an efficient way for researchers and commercial data consumers to collect high quality personal health data for research and commercial purposes.

Although most online learning environments are predominately text based, researchers have argued that representational support for the conceptual structure of a problem would address problems of coherence and convergence that have been shown to be associated with threaded discussions and more effectively support collaborative knowledge construction. The study described in this paper sets out to investigate the merits of knowledge mapping representations as an adjunct to or replacement for threaded discussion in problem solving by asynchronously communicating dyads. Results show that users of knowledge maps created more hypotheses earlier in the experimental sessions and elaborated on them more than users of threaded discussions. Participants using knowledge maps were more likely to converge on the same conclusion and scored significantly higher on post-test questions that required integration of information distributed across dyads in a hidden profile design, suggesting that there was greater collaboration during the session. These results were most consistent when a knowledge map with embedded notes was the primary means of interaction rather than when it augmented a threaded discussion.The paper also offers a methodological contribution: a paradigm for practical experimental study of asynchronous collaboration. It is crucial to understand how to support collaborative knowledge construction in the asynchronous settings prevalent in online learning, yet prior experimental research has focused on face-to-face and synchronous collaboration due to the pragmatic problems of conducting controlled studies of asynchronous interaction. A protocol is outlined that enables study of asynchronous collaboration in a controlled setting.

Both candidates and voters have increased their use of the Internet for political campaigns. Candidates have adopted many internet tools, including social networking websites, for the purposes of communicating with constituents and voters, collecting donations, fostering community, and organizing events. On the other side, voters have adopted Internet tools such as blogs and social networking sites to relate to candidates, engage in political dialogue, pursue activist causes, and share information. In this paper we examine two years of posts on the Facebook walls of the three major contenders for the U.S. Presidency in 2008: Barack Obama, Hillary Clinton, and John McCain. We analyze participation patterns of usage along dimensions of breadth and frequency, and interpret them in terms of the concept of the "public sphere".

Bitcoin is a cryptocurrency whose transactions are recorded on a distributed, openly accessible ledger. On the Bitcoin Blockchain, an entity's real-world identity is hidden behind a pseudonym, a so-called address. Therefore, Bitcoin is widely assumed to provide a high degree of anonymity, which is a driver for its frequent use for illicit activities. This paper presents a novel approach for reducing the anonymity of the Bitcoin Blockchain by using Supervised Machine Learning to predict the type of yet-unidentified entities. We utilised a sample of 434 entities (with ≈ 200 million transactions), whose identity and type had been revealed, as training set data and built classifiers differentiating among 10 categories. Our main finding is that we can indeed predict the type of a yet-unidentified entity. Using the Gradient Boosting algorithm, we achieve an accuracy of 77% and F1-score of ≈ 0.75. We discuss our novel approach of Supervised Machine Learning for uncovering Bitcoin Blockchain anonymity and its potential applications to forensics and financial compliance and its societal implications, outline study limitations and propose future research directions.

Current analytical approaches in computational social science can be characterized by four dominant paradigms: text analysis (information extraction and classification), social network analysis (graph theory), social complexity analysis (complex systems science), and social simulations (cellular automata and agent-based modeling). However, when it comes to organizational and societal units of analysis, there exists no approach to conceptualize, model, analyze, explain, and predict social media interactions as individuals' associations with ideas, values, identities, and so on. To address this limitation, based on the sociology of associations and the mathematics of set theory, this paper presents a new approach to big data analytics called social set analysis. Social set analysis consists of a generative framework for the philosophies of computational social science, theory of social data, conceptual and formal models of social data, and an analytical framework for combining big social data sets with organizational and societal data sets. Three empirical studies of big social data are presented to illustrate and demonstrate social set analysis in terms of fuzzy set-theoretical sentiment analysis, crisp set-theoretical interaction analysis, and eventstudies-oriented set-theoretical visualizations. Implications for big data analytics, current limitations of the set-theoretical approach, and future directions are outlined.INDEX TERMS Big social data, formal models, social set analysis, big data visual analytics, new computational models for big social data.