Correction to: User interest community detection on social media using collaborative filtering

Jiang, Liang; Shi, Leilei; Liu, Lu; Yao, Jingjing; Yousuf, Muhammad Ali

doi:10.1007/s11276-019-01981-0

Cited by 12 publications

(2 citation statements)

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“…The development of social media determines the emergence of new trends in journalism, its new types (Jiang et al, 2019). The existence of online journalism as a separate industry is also confirmed by the fact that thanks to the platform of the Web 2.0, everyone can become an author of an information product, that is, a journalist.…”

Section: Discussionmentioning

confidence: 99%

“…is the most common usage as the use of mobile Internet technologies, which are based on the ideological and technological foundations of the second web, which allows users to create content and share it (Littman et al, 2018). A lot of authors are studying the phenomenon of social media, and their number is constantly growing, due to the development of this sphere in general and citizen journalism in particular (Jiang et al, 2019). Social media uses information technology to turn communication into an interactive dialogue (Niklewicz, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

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Social Component of Journalism and Information Technology in Kazakhstan

Sauyrbaeva¹,

Omarov²,

Kulibekova³

et al. 2020

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The relevance of the study is based on the fact that in sociology, the phenomenon of mass media is included in the object-subject field of several industries: sociology of mass media, sociology of the media means, sociology of journalism, sociology of the Internet, and sociology of online media. The novelty of the work is determined by the fact that the analysis of the media was carried out in the framework of various sociological theories, in particular -mass society theory, where the media are considered as one of the main tools for disseminating mass culture; theories of the information society, within which information and the means of its dissemination play a vital role in the development of society. The practical novelty of the study is determined by the fact that the possibility of forming a structure for understanding the social position of the media contributes to the implementation of new social communication protocols.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Social Component of Journalism and Information Technology in Kazakhstan

Sauyrbaeva¹,

Omarov²,

Kulibekova³

et al. 2020

View full text Add to dashboard Cite

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An efficient algorithm for link prediction based on local information: Considering the effect of node degree

Mumin

Shi

Liu

2021

Concurrency and Computation

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There has been much interest in link prediction research with significant studies on how to predict missing links or future links in a network based on observed information. However, the key solution to tackle the link prediction problem is how to measure the similarity between the nodes in a network with higher accuracy. Several techniques have been proposed that utilize the similarity between nodes to estimate their proximity in the network. In this paper, an efficient link prediction algorithm that predicts relationships between links using the network structure is proposed. This algorithm uses common neighbors in addition to the degree distribution of the nodes to estimate the possibility of the presence of a link between two nodes in a network based on local information. Extensive experiments are carried out and compared with 12 standard similarity-based methods using seven real-world datasets. The experimental results show that our method has higher prediction accuracy compared with most of the local information based methods like the Common Neighbor and Preferential Attachment.It is also competitive with the quasi-local indicators such as LP and global indicators like Katz, with a lower computational complexity than the two.

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Mobile healthcare data mining for sport item recommendation in edge-cloud collaboration

Chen

Duan

2022

Wireless Netw

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Correction to: User interest community detection on social media using collaborative filtering

Cited by 12 publications

References 0 publications

Social Component of Journalism and Information Technology in Kazakhstan

Social Component of Journalism and Information Technology in Kazakhstan

An efficient algorithm for link prediction based on local information: Considering the effect of node degree

Mobile healthcare data mining for sport item recommendation in edge-cloud collaboration

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