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Social Network Analysis and Mining

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01.12.2022 | Review Paper

A multi-perspective micro-analysis of popularity trend dynamics for user-generated content

verfasst von: Divya Jatain, Vikram Singh, Naveen Dahiya

Erschienen in: Social Network Analysis and Mining | Ausgabe 1/2022

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Abstract

With the advancements in user-generated content sites, their prominence for multimedia information dissemination is increasing on a rapid scale. An information about which content is going to be popular and to what extent has a big set of application area including load balancing of servers, online marketing strategy decisions, recommendation systems, disaster management, etc. Big tech-giants, to gain a competitive advantage for strategic decision making, are employing various techniques for an accurate and efficient popularity prediction mechanism based on past trends and current scenario. Thus, there is a need for a detailed and comprehensive study in the field of popularity prediction so as to identify the current trends, in order to tap benefits from the huge potential the field offers. This paper deals with a detailed survey of the literature of popularity prediction of online content covering all the aspects of popularity and the interplay between the contents at various platforms, relationship between the granularity levels and various feature selection methods used for different approaches to prediction. A bibliometrics analysis of the selected approaches is also conducted with a discussion of their advantages and limitations. This will help the researchers to focus on emerging trends of online content popularity prediction algorithms.

Vorheriger Artikel A Survey on Social Media Influence Environment and Influencers Identification

Nächster Artikel Detection of dangerous events on social media: a critical review

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Titel: A multi-perspective micro-analysis of popularity trend dynamics for user-generated content
verfasst von: Divya Jatain
Vikram Singh
Naveen Dahiya
Publikationsdatum: 01.12.2022
Verlag: Springer Vienna
Erschienen in: Social Network Analysis and Mining / Ausgabe 1/2022
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI: https://doi.org/10.1007/s13278-022-00969-7

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