Examining the criterion-related validity evidence of LinkedIn profile elements in an applied sample

Cubrich, Marc; King, Rachel T.; Mracek, Derek L.; Strong, Jamie M.G.; Hassenkamp, Kristen; Vaughn, Daly; Dudley, Nikki M.

doi:10.1016/j.chb.2021.106742

Cited by 32 publications

(27 citation statements)

References 46 publications

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“…As the use of SNSs has increased decade by decade, so has interest in using SNSs in recruitment and hiring processes [3]. In this sense, for [28], LinkedIn is the most influential web tool in terms of professional use, unlike other SNSs such as Facebook or Twitter, which focus on social relationships.…”

Section: Refmentioning

confidence: 99%

“…It is also a tool that can be used to find relevant, quality content. According to [3], allowing users to provide written recommendations and endorsements of skills that appear on a user's profile, LinkedIn has also introduced endorsements, in which users are asked to endorse the skills of other users [27].…”

Section: Refmentioning

confidence: 99%

“…The data contained in SNSs are very diverse [3], and RSs also obtain information from multiple sources; to handle heterogeneity, they have adopted semantic knowledge representation as one of the theories to help deal with this [26].…”

Section: Refmentioning

confidence: 99%

“…Nowadays, organizations can use different Social Networks Sites (SNSs) to research into candidates [3], as well as to gather information on labor trends so as to adjust academic plans, analyze student profiles and extract their skills, and identify the most desired skills for the purpose of adjusting curricula [4].…”

Section: Introduction and Related Studiesmentioning

confidence: 99%

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Using LinkedIn Endorsements to Reinforce an Ontology and Machine Learning-Based Recommender System to Improve Professional Skills

2022

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Nowadays, social networks have become highly relevant in the professional field, in terms of the possibility of sharing profiles, skills and jobs. LinkedIn has become the social network par excellence, owing to its content in professional and training information and where there are also endorsements, which are validations of the skills of users that can be taken into account in the recruitment process, as well as in the recommender system. In order to determine how endorsements influence Lifelong Learning course recommendations for professional skills development and enhancement, a new version of our Lifelong Learning course recommendation system is proposed. The recommender system is based on ontology, which allows modelling the data of knowledge areas and job performance sectors to represent professional skills of users obtained from social networks. Machine learning techniques are applied to group entities in the ontology and make predictions of new data. The recommender system has a semantic core, content-based filtering, and heuristics to perform the formative suggestion. In order to validate the data model and test the recommender system, information was obtained from web-based lifelong learning courses and information was collected from LinkedIn professional profiles, incorporating the skills endorsements into the user profile. All possible settings of the system were tested. The best result was obtained in the setting based on the spatial clustering algorithm based on the density of noisy applications. An accuracy of 94% and 80% recall was obtained.

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Section: Refmentioning

confidence: 99%

Section: Refmentioning

confidence: 99%

Section: Refmentioning

confidence: 99%

Section: Introduction and Related Studiesmentioning

confidence: 99%

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Using LinkedIn Endorsements to Reinforce an Ontology and Machine Learning-Based Recommender System to Improve Professional Skills

2022

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“…LinkedIn is a social media platform with many users that focuses on professionalism and career development [5]. Jobseekers or employers are gathered in one social media platform [6]. LinkedIn connects job seekers with various companies, nationally and internationally [7].…”

Section: Introductionmentioning

confidence: 99%

Face Shape-Based Physiognomy in LinkedIn Profiles with Cascade Classifier and K-Means Clustering

Purwono

Ma’arif

Wulandari

2021

2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)

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The progress of a company is influenced by the excellent performance of its employee. The recruitment process should be done in a correct procedure so that it would not have the potential to harm the company. The improved use of social media can be an aspect to be applied in a recruitment process. LinkedIn is a social media platform that has many users which focuses on the career development aspect. Profile photos are commonly used in social media. In physiognomy, a personality analysis can be carried out based on his/her outward appearance. The profile photo can be an aspect of personality analysis with this knowledge. This research aimed to predict the face shape based on LinkedIn profile photos. A Cascade classifier algorithm with a haar-like feature was used to detect the face area. Dlib library was used to detect face landmarks. K-Means algorithm was used to differentiate the border of hair and facial skin. Indicators of the face shape calculation are the value of face angle, which is the arctangent of the face landmarks matrix; similarity value from the standard deviation calculation between horizontal line 1, 2, and 3; and diameter value which resulted from the standard deviation calculation between horizontal line 2 and vertical line 4. We provide output as face shape from the LinkedIn profile photos. Based on ten profile photo samples, only two predictions were incorrect.

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Cybervetting: Valide Personalauswahl mit sozialen Netzwerken?

Mönke,

Lüdemann,

Schäpers

2023

Personalauswahl 4.0

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Examining the criterion-related validity evidence of LinkedIn profile elements in an applied sample

Cited by 32 publications

References 46 publications

Using LinkedIn Endorsements to Reinforce an Ontology and Machine Learning-Based Recommender System to Improve Professional Skills

Using LinkedIn Endorsements to Reinforce an Ontology and Machine Learning-Based Recommender System to Improve Professional Skills

Face Shape-Based Physiognomy in LinkedIn Profiles with Cascade Classifier and K-Means Clustering

Cybervetting: Valide Personalauswahl mit sozialen Netzwerken?

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