With the advent of modern cognitive computing technologies, fashion informatics researchers contribute to the academic and professional discussion about how a large-scale data set is able to reshape the fashion industry. Data-mining-based social network analysis is a promising area of fashion informatics to investigate relations and information flow among fashion units. By adopting this pragmatic approach, we provide dynamic network visualizations of the case of Paris Fashion Week. Three time periods were researched to monitor the formulation and mobilization of social media users' discussions of the event. Initial textual data on social media were crawled, converted, calculated, and visualized by Python and Gephi. The most influential nodes (hashtags) that function as junctions and the distinct hashtag communities were identified and represented visually as graphs. The relations between the contextual clusters and the role of junctions in linking these clusters were investigated and interpreted.

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Citations in scientific literature are important both for tracking the historical development of scientific ideas and for forecasting research trends. However, the diffusion mechanisms underlying the citation process remain poorly understood, despite the frequent and longstanding use of citation counts for assessment purposes within the scientific community. Here, we extend the study of citation dynamics to a more general diffusion process to understand how citation growth associates with different diffusion patterns. Using a classic diffusion model, we quantify and illustrate specific diffusion mechanisms which have been proven to exert a significant impact on the growth and decay of citation counts. Experiments reveal a positive relation between the "low p and low q" pattern and high scientific impact. A sharp citation peak produced by rapid change of citation counts, however, has a negative effect on future impact. In addition, we have suggested a simple indicator, saturation level, to roughly estimate an individual article's current stage in the life cycle and its potential to attract future attention. The proposed approach can also be extended to higher levels of aggregation (e.g., individual scientists, journals, institutions), providing further insights into the practice of scientific evaluation.

Purpose The purpose of this paper is to investigate the continued use behavior (CU) of link sharing tools based on uses and gratifications theory, the theory of planned behavior and expectation confirmation theory. It then builds a conceptual model that is empirically tested. Design/methodology/approach Data were collected from 343 students (undergraduates, masters, PhD students, and MBAs) from three Chinese universities via a two-phrase survey. The tools SPSS 18.0 and AMOS 18.0 were used to analyse the reliability, validity, model fits and SEM, respectively. Findings The results indicate that an individual’s CU of link sharing tools was determined by his or her continued use intention directly and subjective norm indirectly. Users’ satisfaction on link sharing tools was the main factor affecting the continuance intention. Individuals’ motivation needs such as cognitive needs, personal integrative needs, and social integrative needs were found to be the significant predictors of his or her satisfaction. Besides, people with high privacy concern tended to have less satisfaction with link sharing tools. Originality/value This study explores users’ CU of link sharing tools in social media for the first time. The theoretical model developed shows the predictors behind people’s CU.

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Scientific novelty drives the efforts to invent new vaccines and solutions during the pandemic. First-time collaboration and international collaboration are two pivotal channels to expand teams' search activities for a broader scope of resources required to address the global challenge, which might facilitate the generation of novel ideas. Our analysis of 98,981 coronavirus papers suggests that scientific novelty measured by the BioBERT model that is pretrained on 29 million PubMed articles, and first-time collaboration increased after the outbreak of COVID-19, and international collaboration witnessed a sudden decrease. During COVID-19, papers with more first-time collaboration were found to be more novel and international collaboration did not hamper novelty as it had done in the normal periods. The findings suggest the necessity of reaching out for distant resources and the importance of maintaining a collaborative scientific community beyond nationalism during a pandemic.

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