Search

Main path analysis is a popular method for extracting the scientific backbone from the citation network of a research domain. Existing approaches ignored the semantic relationships between the citing and cited publications, resulting in several adverse issues, in terms of coherence of main paths and coverage of significant studies. This paper advocated the semantic main path network analysis approach to alleviate these issues based on citation function analysis. A wide variety of SciBERT‐based deep learning models were designed for identifying citation functions. Semantic citation networks were built by either including important citations, for example, extension, motivation, usage and similarity, or excluding incidental citations like background and future work. Semantic main path network was built by merging the top‐K main paths extracted from various time slices of semantic citation network. In addition, a three‐way framework was proposed for the quantitative evaluation of main path analysis results. Both qualitative and quantitative analysis on three research areas of computational linguistics demonstrated that, compared to semantics‐agnostic counterparts, different types of semantic main path networks provide complementary views of scientific knowledge flows. Combining them together, we obtained a more precise and comprehensive picture of domain evolution and uncover more coherent development pathways between scientific ideas. [ABSTRACT FROM AUTHOR]

This article considers the interdisciplinary opportunities and challenges of working with digital cultural heritage, such as digitized historical newspapers, and proposes an integrated digital hermeneutics workflow to combine purely disciplinary research approaches from computer science, humanities, and library work. Common interests and motivations of the above‐mentioned disciplines have resulted in interdisciplinary projects and collaborations such as the NewsEye project, which is working on novel solutions on how digital heritage data is (re)searched, accessed, used, and analyzed. We argue that collaborations of different disciplines can benefit from a good understanding of the workflows and traditions of each of the disciplines involved but must find integrated approaches to successfully exploit the full potential of digitized sources. The paper is furthermore providing an insight into digital tools, methods, and hermeneutics in action, showing that integrated interdisciplinary research needs to build something in between the disciplines while respecting and understanding each other's expertise and expectations. [ABSTRACT FROM AUTHOR]

File sharing is an integral component of modern work. Files can be shared either using Group Information Management (GIM), where collaborators exploit a common repository (e.g., the cloud), or Personal Information Management (PIM), where files are sent via email attachments, and collaborators store files individually in personal collections. Given the recent prevalence of GIM, we compare the effects on retrieval for PIM versus GIM collections. We examine the effects of various theoretically motivated factors relating to collection size, properties of the target file, and user workload. In our study, 289 participants accessed 1,557 of their own shared files in a naturalistic setting. Results indicate that factors relating to collection size, file versions, and user workload negatively affect the retrieval of GIM more than PIM files, indicating that PIM is more scalable than GIM. Testing a very different population, we confirm previous findings that failure percentages of GIM are approximately double those of PIM. We discuss possible theoretical explanations, specifically how factors that hinder retrieval exacerbate the general GIM problem of retrieving files organized by other people. Overall, PIM's greater scalability has practical implications for fast‐growing organizations such as startups when choosing their sharing policies. [ABSTRACT FROM AUTHOR]

Surveillance, as a concept and social practice, is inextricably linked to information. It is, at its core, about information extraction and analysis conducted for some regulatory purpose. Yet, information science research only sporadically leverages surveillance studies scholarship, and we see a lack of sustained and focused attention to surveillance as an object of research within the domains of information behavior and social informatics. Surveillance, as a range of contextual and culturally based social practices defined by their connections to information seeking and use, should be framed as information practice—as that term is used within information behavior scholarship. Similarly, manifestations of surveillance in society are frequently perfect examples of information and communications technologies situated within everyday social and organizational structures—the very focus of social informatics research. The technological infrastructures and material artifacts of surveillance practice—surveillance technologies—can also be viewed as information tools. Framing surveillance as information practice and conceptualizing surveillance technologies as socially and contextually situated information tools can provide space for new avenues of research within the information sciences, especially within information disciplines that focus their attention on the social aspects of information and information technologies in society. [ABSTRACT FROM AUTHOR]