期刊信息

New Review of Hypermedia and Multimedia

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影响因子:
0.8
出版商:
Taylor & Francis
ISSN:
1361-4568
浏览:
15771
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0
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征稿

New Review of Hypermedia and Multimedia is an academic journal published by Taylor & Francis. (ISSN 1361-4568, impact factor 0.8).

Aims and scope The New Review of Hypermedia and Multimedia (NRHM) is a world-leading interdisciplinary journal providing a focus for research covering practical and theoretical developments in hypermedia, hypertext, the Web and interactive multimedia. The NRHM welcomes high-quality, original work that advances our knowledge and understanding of the field. Review articles and surveys which present new insights into the field, will also be considered. Topics relevant to the NRHM include, but are not limited to, the following key themes: the conceptual basis of hypertext systems; link metrics; link dynamics; time and synchronisation; the semantic web; the internet of things (IoT); linked data; Web of things; Web of Data; cognitive aspects; hypertext reading practices, navigation and browsing; studies of information seeking behaviour; authoring and design strategies; digital storytelling; authoring hypertext, multimedia authoring tools; automatic authoring; archiving; intelligent and adaptive hypermedia; social recommender systems; personalisation; user modelling; social media algorithms; knowledge organization systems and services; search systems; visualisation; user interfaces; multi-modal interaction; user experience design; affective design; gamification; usability; accessibility; testing and evaluation; individual, social and societal implications; social media; socio-technical systems; diversity and inclusivity; privacy; network effects. physical hypermedia; mobile and wearable applications; geolocation-based applications; immersive, augmented and virtual environments; hypermedia applications in specific contexts, e.g. education, commerce, health, government, culture; literary and creative hypermedia; artificial intelligence in hypertext and hypermedia; Artificial Intelligence, deep learning, machine learning and large language models only as applied to hypertext and hypermedia. All submitted manuscripts are subject to initial appraisal by the Editors-in-Chief, and, if found suitable for further consideration, to rigorous peer-review by independent, anonymous expert referees. All peer review is single anonymized and submissions can be made via the NRHM submission site . Queries regarding submissions can be made by contacting the Editors-in-Chief, whose decision is final.
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