| dc.description.abstract | As an emerging technology, blockchain is transforming a variety of industries and attracting academic interests. However, from the perspectives of methodology and data sources, in-depth understandings of blockchain development are still lacking. Hence, this dissertation aims to further understand the development of blockchain technology by discovering key hidden patterns and relationships with the data source of quality research papers and news. A total of 3,841 blockchain-related scholarly papers and 36,364 news during the period of 2016–2020 were retrieved from the Web of Science and LexisNexis Academic databases, respectively. By using concept linking analysis, this study identified the main concepts and semantic structures associated with blockchain papers and news. While ‘technology’, ‘transaction’, ‘privacy and security’, ‘environment’, and ‘consensus’ were the most strongly concepts associated with blockchain in research, terms such as ‘platform’, ‘big data and cloud’, ‘network’, ‘healthcare and business’, and ‘authentication’ were closely related to the blockchain news. To further understand the similarities and differences between blockchain research and news, this paper classified concepts into five patterns including hardware and infrastructure, data, networking, applications, and consensus. Besides, various network modeling analyses such as keyword co-occurrence, bibliographic coupling, co-citation, and co-authorship were conducted to explore different relationship perspectives of blockchain development. Such findings also helped us understand the top research topics, influential authors, leading venues, publication institutes, and countries. This dissertation assists scholars and practitioners in gaining a complete overview of the present status of blockchain research and its tendencies. | en_US |