Vritthi - a theoretical framework for IT recruitment based on machine learning techniques applied over Twitter, LinkedIn, SPOJ and GitHub profiles

Giri, Animesh; Ravikumar, Abhiram; Mote, Sneha R.; Bharadwaj, Rahul

doi:10.1109/sapience.2016.7684163

Cited by 10 publications

(3 citation statements)

References 4 publications

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“…While some researchers have assessed personality traits of candidates based on their social media usage (Abel et al , 2017; Faliagka et al , 2012; Menon and Rahulnath, 2016), others have measured their skills, qualifications and achievements against job requirements (Faliagka et al , 2014; Jantan et al , 2010; Truică and Barnoschi, 2015). Giri et al (2016) developed a comprehensive recruitment model assessing personality traits and professional skills using candidates' information from Twitter, LinkedIn, GitHub and SPOJ. Such models offer MNCs the promise of a comprehensive and bias-reduced process that can evaluate applicants across all talent pools on pre-established parameters.…”

Section: Detailed Analysis Of the Resultsmentioning

confidence: 99%

A review of machine learning applications in human resource management

Garg

Sinha

Kar

et al. 2021

IJPPM

147

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PurposeThis paper reviews 105 Scopus-indexed articles to identify the degree, scope and purposes of machine learning (ML) adoption in the core functions of human resource management (HRM).Design/methodology/approachA semi-systematic approach has been used in this review. It allows for a more detailed analysis of the literature which emerges from multiple disciplines and uses different methods and theoretical frameworks. Since ML research comes from multiple disciplines and consists of several methods, a semi-systematic approach to literature review was considered appropriate.FindingsThe review suggests that HRM has embraced ML, albeit it is at a nascent stage and is receiving attention largely from technology-oriented researchers. ML applications are strongest in the areas of recruitment and performance management and the use of decision trees and text-mining algorithms for classification dominate all functions of HRM. For complex processes, ML applications are still at an early stage; requiring HR experts and ML specialists to work together.Originality/valueGiven the current focus of organizations on digitalization, this review contributes significantly to the understanding of the current state of ML integration in HRM. Along with increasing efficiency and effectiveness of HRM functions, ML applications improve employees' experience and facilitate performance in the organizations.

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Section: Detailed Analysis Of the Resultsmentioning

confidence: 99%

A review of machine learning applications in human resource management

Garg

Sinha

Kar

et al. 2021

IJPPM

147

View full text Add to dashboard Cite

show abstract

“…Moreover, Domeniconi et al (Domeniconi et al 2016) used the data from LinkedIn users' public profiles to identify relationships between jobs and people skills. Giri et al (Giri et al 2016) developed a hassle-free automated process that enables recruiters to select candidates who fit their organization. Using Greedy, Hierarchical, and K Mean clustering algorithms, Garg et al (Garg, Rani, and Miglani 2016) clustered LinkedIn profiles by job title, company name, and geography.…”

Section: Literature Reviewmentioning

confidence: 99%

Data-Driven Deep Journalism to Discover Age Dynamics in Multi-Generational Labour Markets from LinkedIn Media

Alaql¹,

Alqurashi²,

Mehmood³

2022

Preprint

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We live in the information age and, ironically, meeting the core function of journalism – i.e., to provide people access to unbiased information – has never been more difficult. This paper explores deep journalism, our data-driven Artificial Intelligence (AI) based journalism approach to study how the LinkedIn media could be useful for journalism. Specifically, we apply our deep journalism approach to LinkedIn to automatically extract and analyse big data to provide the public with information about labour markets, people’s skills and education, and businesses and industries from multi-generational perspectives. The Great Resignation and Quiet Quitting phenomena coupled with rapidly changing generational attitudes are bringing unprecedented and uncertain changes to labour markets and our economies and societies, and hence the need for journalistic investigations into these topics is highly significant. We combine big data and machine learning to create a whole machine learning pipeline and a software tool for journalism that allows discovering parameters for age dynamics in labour markets using LinkedIn data. We collect a total of 57,000 posts from LinkedIn and use it to discover 15 parameters by Latent Dirichlet Allocation algorithm (LDA) and group them into five macro-parameters, namely Generations-Specific Issues, Skills & Qualifications, Employment Sectors, Consumer Industries, and Employment Issues. The journalism approach used in this paper can automatically discover and make objective, cross-sectional, and multi-perspective information available to all. It can bring rigour to journalism by making it easy to generate information using machine learning and can make tools and information available so that anyone can uncover information about matters of public importance. This work is novel since none of the earlier works have reported such an approach and tool and leveraged it to use LinkedIn media for journalism and to discover multigenerational perspectives (parameters) for age dynamics in labour markets. The approach could be extended with additional AI tools and other media.

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“…Previous research in the domain of this paper has employed NLP-based topic modelling for trend analysis, [32][33][34][35] addressing challenges [36][37][38] and sentiment analysis. 39,40 Research in social media and LinkedIn mining has focused on revealing demographic insights, 41 employee skills 42,43 and job 44 and profile classifications. [45][46][47][48][49][50][51] Additionally, Twitter data has been harnessed for appraising transit service quality, 52 detecting traffic-related events, 53 emergency management 54 and conducting temporal trend analysis.…”

Section: Introductionmentioning

confidence: 99%

Multi-generational labour markets: Data-driven discovery of multi-perspective system parameters using machine learning

Alaql,

Alqurashi,

Mehmood

2023

Science Progress

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The impact of aggressive capitalist approaches on social, economic and planet sustainability is significant. Economic issues such as inflation, energy costs, taxes and interest rates persist and are further exacerbated by global events such as wars, pandemics and environmental disasters. A sustained history of financial crises exposes weaknesses in modern economies. The Great Attrition, with many quitting jobs, adds to concerns. The diversity of the workforce poses new challenges. Transformative approaches are essential to safeguard societies, economies and the planet. In this work, we use big data and machine learning methods to discover multi-perspective parameters for multi-generational labour markets. The parameters for the academic perspective are discovered using 35,000 article abstracts from the Web of Science for the period 1958–2022 and for the professionals’ perspective using 57,000 LinkedIn posts from 2022. We discover a total of 28 parameters and categorized them into five macro-parameters, Learning & Skills, Employment Sectors, Consumer Industries, Learning & Employment Issues and Generations-specific Issues. A complete machine learning software tool is developed for data-driven parameter discovery. A variety of quantitative and visualization methods are applied and multiple taxonomies are extracted to explore multi-generational labour markets. A knowledge structure and literature review of multi-generational labour markets using over 100 research articles is provided. It is expected that this work will enhance the theory and practice of artificial intelligence-based methods for knowledge discovery and system parameter discovery to develop autonomous capabilities and systems and promote novel approaches to labour economics and markets, leading to the development of sustainable societies and economies.

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Vritthi - a theoretical framework for IT recruitment based on machine learning techniques applied over Twitter, LinkedIn, SPOJ and GitHub profiles

Cited by 10 publications

References 4 publications

A review of machine learning applications in human resource management

A review of machine learning applications in human resource management

Data-Driven Deep Journalism to Discover Age Dynamics in Multi-Generational Labour Markets from LinkedIn Media

Multi-generational labour markets: Data-driven discovery of multi-perspective system parameters using machine learning

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