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pro vyhledávání: '"popularity prediction"'
Akademický článek
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Autor:
Chao QIU, Chenyang WANG
Publikováno v:
天地一体化信息网络, Vol 5, Pp 40-47 (2024)
This paper proposed a new space-air-ground integrated network caching method, that was, cluster based content caching method.Based on the space-air-ground integrated network architecture, this method used the extracted content features to predict the
Externí odkaz:
https://doaj.org/article/3c3663ddb5c34c3987c8a113912fcd01
Publikováno v:
IEEE Access, Vol 12, Pp 47508-47517 (2024)
In order to furnish valuable insights and solutions applicable to content creators, social media platforms, academic research, and general users, this investigation integrates the Internet of Things (IoT) with deep learning regression models to exami
Externí odkaz:
https://doaj.org/article/7d8d20f7f113498fa1cdc0a23ad442df
Publikováno v:
Symmetry, Vol 16, Iss 11, p 1436 (2024)
Online social media have significantly boosted the creation and transmission of information, accelerating the dissemination and interaction of vast amounts of data, thereby making the prediction of information cascades increasingly important. In rece
Externí odkaz:
https://doaj.org/article/2f8164f91991485b88b1a9ac94367be9
Examining visual impact: predicting popularity and assessing social media visual strategies for NGOs
Publikováno v:
Online Media and Global Communication, Vol 2, Iss 4, Pp 594-620 (2023)
This research aims to analyze the role of visuals posted on the social media of NGOs and to predict the popularity of a post based on the characteristics of the visual it contains.
Externí odkaz:
https://doaj.org/article/23258081698c4017af3ebdf13b2c76e5
Publikováno v:
PeerJ Computer Science, Vol 10, p e1870 (2024)
It is critical to accurately predict the future popularity of information cascades for many related applications, such as online opinion warning or academic influence evaluation. Despite many efforts devoted to developing effective prediction approac
Externí odkaz:
https://doaj.org/article/4549de8bb6514f42baf73404739d8e40
Autor:
Khushnood Abbas, Mohammad Kamrul Hasan, Alireza Abbasi, Umi Asma Mokhtar, Asif Khan, Siti Norul Huda Sheikh Abdullah, Shi Dong, Shayla Islam, Dabiah Alboaneen, Fatima Rayan Awad Ahmed
Publikováno v:
IEEE Access, Vol 11, Pp 83052-83068 (2023)
One of the key goals of Informetrics is to identify citation-based popular articles among so many other aspects, such as determining popular research topics, identifying influential scholars, and predicting hot trends in science. These can be achieve
Externí odkaz:
https://doaj.org/article/69135ac22a204af99e3421331b0aa0e5
Autor:
Amin Omidvar, Aijun An
Publikováno v:
IEEE Access, Vol 11, Pp 60904-60914 (2023)
Headlines are not only essential for summarizing news articles but also for grabbing users’ attention. Headline generation is a type of text summarization that can employ either an extractive or abstractive approach, with the latter being more prev
Externí odkaz:
https://doaj.org/article/6dfc5d6d1305462dad9d7b0c8e4c9044
Autor:
Khushnood Abbas, Mohammad Kamrul Hasan, Alireza Abbasi, Shi Dong, Taher M. Ghazal, Siti Norul Huda Sheikh Abdullah, Asif Khan, Dabiah Alboaneen, Fatima Rayan Awad Ahmed, Thowiba E. Ahmed, Shayla Islam
Publikováno v:
IEEE Access, Vol 11, Pp 37546-37559 (2023)
One of the big challenges of our modern life is to find the right items or contents on the Internet and particularly in social media. One way of addressing the information overload problem in social media is to predict the future trends and popularit
Externí odkaz:
https://doaj.org/article/c7b014964b76453a9ad0252711453321
Autor:
An-An Liu, Xiaowen Wang, Ning Xu, Junbo Guo, Guoqing Jin, Quan Zhang, Yejun Tang, Shenyuan Zhang
Publikováno v:
Visual Informatics, Vol 6, Iss 4, Pp 78-89 (2022)
With the popularization of social media, the way of information transmission has changed, and the prediction of information popularity based on social media platforms has attracted extensive attention. Feature fusion-based media popularity prediction
Externí odkaz:
https://doaj.org/article/81019125399347eaa6866cfe6aabe960