Entity Resolution in Online Multiple Social Networks (@Facebook and LinkedIn)

Mishra, Ravita

doi:10.1007/978-981-13-1498-8_20

Cited by 8 publications

(2 citation statements)

References 25 publications

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“…However, this negative perception improves if candidates can control their privacy on their network and decide which information shall be visible to other users. Mishra (2019) states that Facebook presents the details of the social activities of a given candidate, while LinkedIn provides an overview of his/her professional relationships and work experience. Henderson (2019) surveyed 195 recruitment specialists in the USA in order to examine the indicators by means of which the activities on social networks are evaluated and those that lead to excluding a candidate from the selection procedure.…”

Section: Literary Researchmentioning

confidence: 99%

Digital Recruitment through Social Networks

Podhorcová

Balková

2023

Acad. J. Interdiscip. Stud.

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OBJECTIVE: Social networks have become a phenomenon of modern times, and their popularity is still growing. Platforms of these media provide space for various marketing strategies, including human resources management. The paper submitted focuses on the role of social networks, namely Facebook and LinkedIn, in the job search process from the perspective of a jobseeker. The objective of the paper is to describe the ways users use these networks for this purpose, to analyse their trend, and compare the results obtained with the results presented in the Scottish study “Using social media during job search” by John A. Mowbray and Hazel Hall (2021). METHODS: The basic data source is responses obtained through an online questionnaire and interviews showing the personal experience of four respondents with the networks under review. Based on the response, a comparison with the relevant findings published in the study by Mowbray and Hall (2021) was carried out. RESULTS: The results show the growing popularity of using Facebook and LinkedIn in job seeking from the side of jobseekers and point to the fact that the type of job searched influences user behaviour. These networks enable both active and passive search, represent a comfortable environment for jobseekers, and are used for building thematic groups.CONCLUSION: The contribution of this paper is the recommended implementation of Facebook and LinkedIn in the job search and recruitment process in order to maximize the efficiency of these processes. The authors also point out the lack of adequate studies concerning users of these networks in the Czech Republic. Received: 30 August 2022 / Accepted: 6 December 2022 / Published: 5 January 2023

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Section: Literary Researchmentioning

confidence: 99%

Digital Recruitment through Social Networks

Podhorcová

Balková

2023

Acad. J. Interdiscip. Stud.

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“…Finally, the above two attributes are combined to identify the user. Mishra [20] developed a pairwise comparison string model that analyzes user profile information among different social networks and adopts different similarity 2…”

Section: Related Workmentioning

confidence: 99%

Exploiting Two‐Level Information Entropy across Social Networks for User Identification

Xing

Deng

et al. 2021

Wireless Communications and Mobile Computing

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As the popularity of online social networks has grown, more and more users now hold multiple virtual accounts at the same time. Under these circumstances, identifying multiple social accounts belonging to the same user across different social networks is of great importance for many applications, such as user recommendation, personalized services, and information fusion. In this paper, we mainly aggregate user profile information and user behavior information, then measures and analyzes the attributes contained in these two types of information to implement across social networks user identification. Moreover, as different user attributes have different effects on user identification, this paper therefore proposes a two-level information entropy-based weight assignment method (TIW) to weigh each attribute. Finally, we combine the scoring formula with the bidirectional stable marriage matching algorithm to achieve optimal user account matching and thereby obtain the final matching pairs. Experimental results demonstrate that the proposed two-level information entropy method yields excellent performance in terms of precision rate, recall rate, F -measure ( F 1 ), and area under curve (AUC).

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Efficient and Scalable Job Recommender System Using Collaborative Filtering

Mishra¹,

Rathi²

2020

Lecture Notes in Electrical Engineering

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Entity Resolution in Online Multiple Social Networks (@Facebook and LinkedIn)

Cited by 8 publications

References 25 publications

Digital Recruitment through Social Networks

Digital Recruitment through Social Networks

Exploiting Two‐Level Information Entropy across Social Networks for User Identification

Efficient and Scalable Job Recommender System Using Collaborative Filtering

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