Survey on Opinion Leader in Social Network using Data Mining

Katarya, Rahul; Gautam, Diksha

doi:10.1109/icaccs.2019.8728445

Cited by 5 publications

(5 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Katarya et al 16 focused on investigating the identification of opinion leaders in social networks by applying data mining techniques. The survey thoroughly reviewed relevant studies and explored various methodologies employed to identify and analyze these opinion leaders.…”

Section: Related Work and Conceptsmentioning

confidence: 99%

“…The studies in References 4,5,14,18 primarily focused on the Big data in social media, but they have yet to include a taxonomy to understand the subject better and classify their articles. Previous research 5,15,16 focused on a specific sector and ignored some aspects, such as security and medical health, machine learning, and artificial intelligence, which we have covered. Some review studies 1,3,5,15,16 have also ignored the assessment factors in this area, which we addressed.…”

mentioning

confidence: 99%

“…Previous research 5,15,16 focused on a specific sector and ignored some aspects, such as security and medical health, machine learning, and artificial intelligence, which we have covered. Some review studies 1,3,5,15,16 have also ignored the assessment factors in this area, which we addressed. Our study aims to address these research gaps and contribute to advancing this field.…”

mentioning

confidence: 99%

See 2 more Smart Citations

A taxonomy and survey of big data in social media

Hemmati,

Arzanagh,

Rahmani

2023

Concurrency and Computation

View full text Add to dashboard Cite

SummaryExamining the particular value of each platform for big data would be difficult because of the variety of social media forms and sizes. Using social media to objectively and subjectively analyze large groups of individuals makes it the most effective tool for this task. There are numerous sources of big data within the organization. Social media can be identified by the interaction and communication it facilitates. Utilizing social media has become a daily occurrence in modern society. In addition, this frequent use generates data demonstrating the importance of researching the relationship between big data and social media. It is because so many internet users are also active on social media. We conducted a systematic literature review (SLR) to identify 42 articles published between 2018 and 2022 that examined the significance of big data in social media and upcoming issues in this field. We also discuss the potential benefits of utilizing big data in social media. Our analysis discovered open problems and future challenges, such as high‐quality data, information accessibility, speed, natural language processing (NLP), and enhancing prediction approaches. As proven by our investigations of evaluation metrics for big data in social media, the distribution reveals that 24% is related to data‐trace, 12% is related to execution time, 21% to accuracy, 6% to cost, 10% to recall, 11% to precision, 11% to F1‐score, and 5% run time complexity.

show abstract

Section: Related Work and Conceptsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

A taxonomy and survey of big data in social media

Hemmati,

Arzanagh,

Rahmani

2023

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…The first step searched for the most influential, active, and engaged (continuous participation) users, and the second step was based on a hierarchical community analysis. Presently, research combining the two methods is also focused on social networks and content publishing communities such as Facebook, Twitter, and Weibo [ 33 , 34 , 35 ].…”

Section: Related Workmentioning

confidence: 99%

Dynamic Robustness of Open-Source Project Knowledge Collaborative Network Based on Opinion Leader Identification

Lei

Zhang

Liu

2021

Entropy

View full text Add to dashboard Cite

A large amount of semantic content is generated during designer collaboration in open-source projects (OSPs). Based on the characteristics of knowledge collaboration behavior in OSPs, we constructed a directed, weighted, semantic-based knowledge collaborative network. Four social network analysis indexes were created to identify the key opinion leader nodes in the network using the entropy weight and TOPSIS method. Further, three degradation modes were designed for (1) the collaborative behavior of opinion leaders, (2) main knowledge dissemination behavior, and (3) main knowledge contribution behavior. Regarding the degradation model of the collaborative behavior of opinion leaders, we considered the propagation characteristics of opinion leaders to other nodes, and we created a susceptible–infected–removed (SIR) propagation model of the influence of opinion leaders’ behaviors. Finally, based on empirical data from the Local Motors open-source vehicle design community, a dynamic robustness analysis experiment was carried out. The results showed that the robustness of our constructed network varied for different degradation modes: the degradation of the opinion leaders’ collaborative behavior had the lowest robustness; this was followed by the main knowledge dissemination behavior and the main knowledge contribution behavior; the degradation of random behavior had the highest robustness. Our method revealed the influence of the degradation of collaborative behavior of different types of nodes on the robustness of the network. This could be used to formulate the management strategy of the open-source design community, thus promoting the stable development of OSPs.

show abstract

“…The research process of ARS was classified and discussed from the aspects of methods, properties, differences, data sets, experimental platforms and evaluation [22]. Gautam et al [23] introduces the concept of "opinion leader" into online social network, and studies its identification and usage in online social network and data mining. In the era of big data, data mining is obviously an important technology to deal with large data sets.…”

Section: Related Workmentioning

confidence: 99%

Sparse Trust Data Mining

Nie

Jiao

et al. 2021

IEEE Trans.Inform.Forensic Secur.

View full text Add to dashboard Cite

As recommendation systems continue to evolve, researchers are using trust data to improve the accuracy of recommendation prediction and help users find relevant information. However, large recommendation systems with trust data suffer from the sparse trust problem , which leads to grade inflation and severely affects the reliability of trust propagation. This paper presents a novel research on sparse trust data mining, which includes the new concept of sparse trust, a sparse trust model, and a trust mining framework. It lays a foundation for the trust-related research in large recommended systems. The new trust mining framework is based on customized normalization functions and a novel transitive gossip trust model, which discovers potential trust information between entities in a large-scale user network and applies it to a recommendation system. We conducts a comprehensive performance evaluation on both real-world and synthetic datasets. The results confirm that our framework mines new trust and effectively ameliorates sparse trust problem.

show abstract

Survey on Opinion Leader in Social Network using Data Mining

Cited by 5 publications

References 20 publications

A taxonomy and survey of big data in social media

A taxonomy and survey of big data in social media

Dynamic Robustness of Open-Source Project Knowledge Collaborative Network Based on Opinion Leader Identification

Sparse Trust Data Mining

Contact Info

Product

Resources

About