Connectedness, Engagement, and Learning through Social Work Communities on LinkedIn

Mesa, Joaquín Castillo de; Gómez-Jacinto, Luís

doi:10.5093/pi2020a4

Cited by 8 publications

(3 citation statements)

References 39 publications

(40 reference statements)

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“…One of the most used SNS for creating users' business profiles, establishing business contacts, connecting with communities of the same skills and business interests, job searching and e-recruitment is LinkedIn (Tifferet and Vilnai-Yavetz, 2018; Castillo de Mesa and Gómez Jacinto, 2020; Van Dijck, 2013). LinkedIn is often chosen as an e-recruiting tool by companies (Hosain and Liu, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Social recruiting: an application of social network analysis for preselection of candidates

Milovanović

Bogdanović

Лабус

et al. 2022

DTA

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PurposeThe paper aims to studiy social recruiting for finding suitable candidates on social networks. The main goal is to develop a methodological approach that would enable preselection of candidates using social network analysis. The research focus is on the automated collection of data using the web scraping method. Based on the information collected from the users' profiles, three clusters of skills and interests are created: technical, empirical and education-based. The identified clusters enable the recruiter to effectively search for suitable candidates.Design/methodology/approachThis paper proposes a new methodological approach for the preselection of candidates based on social network analysis (SNA). The defined methodological approach includes the following phases: Social network selection according to the defined preselection goals; Automatic data collection from the selected social network using the web scraping method; Filtering, processing and statistical analysis of data. Data analysis to identify relevant information for the preselection of candidates using attributes clustering and SNA. Preselection of candidates is based on the information obtained.FindingsIt is possible to contribute to candidate preselection in the recruiting process by identifying key categories of skills and interests of candidates. Using a defined methodological approach allows recruiters to identify candidates who possess the skills and interests defined by the search. A defined method automates the verification of the existence, or absence, of a particular category of skills or interests on the profiles of the potential candidates. The primary intention is reflected in the screening and filtering of the skills and interests of potential candidates, which contributes to a more effective preselection process.Research limitations/implicationsA small sample of the participants is present in the preliminary evaluation. A manual revision of the collected skills and interests is conducted. The recruiters should have basic knowledge of the SNA methodology in order to understand its application in the described method. The reliability of the collected data is assessed, because users provide data themselves when filling out their social network profiles.Practical implicationsThe presented method could be applied on different social networks, such as GitHub or AngelList for clustering profile skills. For a different social network, only the web scraping instructions would change. This method is composed of mutually independent steps. This means that each step can be implemented differently, without changing the whole process. The results of a pilot project evaluation indicate that the HR experts are interested in the proposed method and that they would be willing to include it in their practice.Social implicationsThe social implication should be the determination of relevant skills and interests during the preselection phase of candidates in the process of social recruitment.Originality/valueIn contrast to previous studies that were discussed in the paper, this paper defines a method for automatic data collection using the web scraper tool. The described method allows the collection of more data in a shorter period. Additionally, it reduces the cost of creating an initial data set by removing the cost of hiring interviewers, questioners and people who collect data from social networks. A completely automated process of data collection from a particular social network stands out from this model from currently available solutions. Considering the method of data collection implemented in this paper, the proposed method provides opportunities to extend the scope of collected data to implicit data, which is not possible using the tools presented in other papers.

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Section: Literature Reviewmentioning

confidence: 99%

Social recruiting: an application of social network analysis for preselection of candidates

Milovanović

Bogdanović

Лабус

et al. 2022

DTA

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“…On a parallel note, this paper provides a robust methodological framework in dealing with heterogeneous data. That is, it presents the design of methods which can be applied to extract, collect, process and analyse data, hence contributing to the wider debate on treating heterogeneous data emanating from social media (Castillo-de Mesa and Gómez-Jacinto 2020 ; Li et al 2014 ; Aggarwal 2011 ). On top of that, we applied standard institutional taxonomic schemes (e.g.…”

Section: Introductionmentioning

confidence: 99%

Lessons for science and technology policy? Probing the Linkedin network of an RDI organisation

et al. 2022

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In this paper, we seek to examine the network of the Greek National Documentation Centre (EKT) as formed by its LinkedIn followers. By applying specific data collection and processing techniques, we explore the network of all the individuals that follow EKT’s LinkedIn page. Significant manual and automatic approaches have been implemented with regard to data extraction, data curation and data homogenization. The aim is to identify the network’s advancement over time, the institutions involved and the countries. The timeframe of the study spans from when the relevant LinkedIn page was constructed in 2015 to 2020. Findings indicate that there is a steady increase in the number of new followers, peaking in 2020. On an international scale, the evolution of the network of followers is imprinted and distributed in worldwide maps. In total, 68 countries have followed EKT over the examined time period. Also, in terms of followers’ institutional sector the Business Sector (BES) stands out (46.5%). Higher Education (HES) and Government Sector (GOV) are associated with 26.4 and 22.2% of the followers, respectively. Lastly, this paper provides a first institutional and country-level mapping of who constitutes the organisation’s interlocutors in the national and global RDI ecosystem.

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“…В таких случаях выделение неявных сообществ на основе взаимодействия пользователей актуально для достижения разных задач. Так, в [17] приведен пример анализа части сети LinkedIn и выявления сообществ среди практиков в сфере социальной работы. Часто сетевые структуры с последующим выделением в них сообществ строятся на основании данных с сайтов размещения вакансий и поиска работников [18].…”

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Implicit Communities Defined on the Graph for Interacting Objects

Чеповский¹

2023

Успехи Кибернетики / Russian Journal of Cybernetics

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в работе рассматриваются текущие и перспективные задачи анализа графов, представляющих сложные сети взаимодействующих объектов. Описаны предметные области, для которых актуальны данные модели, рассматриваются практика, проблемы анализа графов социальных сетей и выделения на них неявных сообществ. Обсуждаются основные алгоритмы выделения сообществ на графах и проблема тестирования качества получаемых разбиений. Автором предложены перспективные, на его взгляд, направления развития данной тематики для графов, полученных при импорте данных из реальных социальных сетей. Показана актуальность подходов, предложенных в предыдущих работах автора. the paper considers the problems of analyzing graphs that represent complex networks of interacting objects. The relevant domains are described, social network graph analysis and implicit community detection are considered. The key graph-based community detection algorithms and the quality assessment of their results are discussed. The author proposed promising development trends using the graphs representing actual social networks. The relevance of the approaches proposed in the author’s previous works is also highlighted.

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Connectedness, Engagement, and Learning through Social Work Communities on LinkedIn

Cited by 8 publications

References 39 publications

Social recruiting: an application of social network analysis for preselection of candidates

Social recruiting: an application of social network analysis for preselection of candidates

Lessons for science and technology policy? Probing the Linkedin network of an RDI organisation

Implicit Communities Defined on the Graph for Interacting Objects

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