Science Models as Value-Added Services for Scholarly Information Systems

The paper introduces scholarly Information Retrieval (IR) as a further dimension that should be considered in the science modeling debate. The IR use case is seen as a validation model of the adequacy of science models in representing and predicting structure and dynamics in science. Particular conceptualizations of scholarly activity and structures in science are used as value-added search services to improve retrieval quality: a co-word model depicting the cognitive structure of a field (used for query expansion), the Bradford law of information concentration, and a model of co-authorship networks (both used for re-ranking search results). An evaluation of the retrieval quality when science model driven services are used turned out that the models proposed actually provide beneficial effects to retrieval quality. From an IR perspective, the models studied are therefore verified as expressive conceptualizations of central phenomena in science. Thus, it could be shown that the IR perspective can significantly contribute to a better understanding of scholarly structures and activities.Comment: 26 pages, to appear in Scientometric

Quantifying trading behavior in financial markets using Google Trends

Crises in financial markets affect humans worldwide. Detailed market data on trading decisions reflect some of the complex human behavior that has led to these crises. We suggest that massive new data sources resulting from human interaction with the Internet may offer a new perspective on the behavior of market participants in periods of large market movements. By analyzing changes in Google query volumes for search terms related to finance, we find patterns that may be interpreted as “early warning signs” of stock market moves. Our results illustrate the potential that combining extensive behavioral data sets offers for a better understanding of collective human behavior

A VOS analysis of LSTM Learners Classification for Recommendation System

In response to the growing popularity of social web apps, much research has gone into analyzing and developing an AI-based responsive suggestion system. Machine learning and neural networks come in many forms that help online students choose the best texts for their studies. However, when training recommendation models to deal with massive amounts of data, traditional machine learning approaches require additional training models. As a result, they are deemed inappropriate for the personalized recommender generation of learning systems. In this paper, we examine LSTM-based strategies in order to make useful recommendations for future research

Security, population and governmentality : UK counter-terrorism discourse (2007-2011)

Over the past decade, governments worldwide have taken initiatives both at a national and supra-national level in order to prevent terrorist attacks from militant groups. This paper analyses a corpus of policy documents which sets out the policy for UK national security. Informed by Foucault’s (2007) theory of governmentality, as well as critical discourse analysis and corpus linguistics, this paper analyses the ways in which the liberal state in late modernity realizes security as discursive practice. A corpus of 110 documents produced by the UK government relating to security in the wake of the 7/7 attacks between 2007 and 2011 was assembled. The paper analyses the discursive constitution of the Foucaultian themes of regulation, knowledge and population, though carrying out a qualitative analysis of relevant key wards, patterns of collocation, as well as features of connotation and semantic prosody

BlogForever D5.3: User Questionnaires and Reports

This report presents the feedback gathered from third party users during the BlogForever Case Studies. Therefore, the research framework is defined and the case studies results are presented, followed by a summary of conclusions and remarks

Exploring the Landscape of Research on Enterprise Green Environments Through Science Mapping Analysis

This study employs science mapping and bibliometric analysis to chart the knowledge structure and research trajectory of enterprise green environment literature from 2002 to 2022. Despite rising interest, comprehensive analyses of this field\u27s research landscapes and dynamics remain scarce. Through advanced techniques including discipline mapping, journal co-citation analysis, author co-citation analysis, and keyword co-occurrence analysis, this work elucidates the prominent disciplines, publications, authors, and research foci in enterprise of green environment scholarship over the past two decades. The results provide vital insights into the current status, influential leaders, core journals, knowledge gaps, and future directions of this rapidly evolving field. This science mapping analysis offers a valuable quantitative overview of green environment research enterprise that can inform scholars worldwide in producing impactful work on this critical area. The findings reveal profound implications for the developing structure and frontiers of sustainability-focused business and management research