=Paper=
{{Paper
|id=Vol-1710/paper25
|storemode=property
|title=Extracting Functional Job Roles from Professional Social Networking Site Profiles
|pdfUrl=https://ceur-ws.org/Vol-1710/paper25.pdf
|volume=Vol-1710
|authors=Anastasiia Nesterenko
|dblpUrl=https://dblp.org/rec/conf/aist/Nesterenko16
}}
==Extracting Functional Job Roles from Professional Social Networking Site Profiles==
https://ceur-ws.org/Vol-1710/paper25.pdf
Extracting Functional Job Roles From
Professional Social Networking Sites Profiles
Anastasiia Nesterenko
National Research University Higher School of Economics
St. Petersburg, Russia,
nastyanestor@gmail.com
1 Introduction
Despite the employment crisis on the Russian job market, demand for the IT
specialists is not stagnating. Growth of flexible specialisation on the market
leaves the trace in skill descriptions[8, p. 119]. Using modern Network Science
and Machine Learning methods, we analysed profile data from business social
networking site MoiKrug.ru and were able to extract skill map and patterns,
characterising functional job roles. This paper is a part of the project is aimed
at comparing signals on two sides of the Russian IT job market: in requirements
extracted from job advertisements and in skills extracted from profiles of poten-
tial employees. At this stage we make an attempt to understand how the supply
is represented, which functional job roles exist, and how they are connected with
each other.
Todd and McKeen[7] performed the analysis of roles and their dynamics
during 1970 - 1990 in the Information Systems area, uncovering three main roles:
computer programmers, systems analysts, information systems managers. The
research was based on the analysis of job advertisements from newspapers. They
showed a growing role of communication and business skills during that period,
compared to knowledge of several programming languages and other technical
skills. As for systems analysts, they needed to grow in both directions, although
the requirement of technical skills had increased dramatically in the mid-80s.
Later Byrd and Turner[1] divided management skills into technology man-
agement skills, business functional skills, interpersonal skills, while classification
of technical skills were stayed unchanged. Noll and Wilkins[4] chose for analysis
of future demand for skills the following occupations: programmers, analysts,
and end-user support. ”Soft skills” continued to play a significant role, while
in technical skills there were some changes toward the web-based languages. In
process of time, more and more attention was paid to technical skills. Litecky
et al.[3], analyzing the job advertisements with the help of statistical tools and
clustering, identified 20 professional categories and their respective skill sets.
Assessing the similarity of skills, the researchers combined more general occupa-
tions: web developers, software developers, database developers, managers (the
largest area), and analysts.
Changes in the demand for skills contribute to the emergence of new pro-
fessions. Debortoli [2] studies sharp rise of Big Data jobs compared to more
II
traditional ”business intelligence” using Latent Semantic Analysis on job adver-
tisements devoted to these areas. They found some similarities and differences in
application areas (about 15), and in required skills. The methods and concepts,
which are specific to BI were: database administration, software engineering,
BI architecture, whereas for Big Data quantitative analysis, machine learning,
database administration, software engineering, software testing and data ware-
housing were more salient.
Another study that has the similar goals and objectives, was the research
of Wowczko I. A. [9]. Based on the selected frequency terms in job titles, were
identified professional subsets: Administrator (keyword: Administrator), Ana-
lyst (keyword: Analyst), Support ( keywords: Engineer), Lead (keywords: Lead,
Manager), Test,Tester, Quality, QA). Using these general categories, 4755 jobs
were classified. During the analysis of their description, was constructed matrix
terms based on ngramms, which to some extent, are similar to skills, although
they are not so clear in comparison with the previous study. In general, the
categories included both technical and managerial disciplines, similarly to our
work.
While these studies show emergence of new specialisations, demonstrating
development of IT area, there is a lack of up-to-date comprehensive skill map of
Russian IT job market, and the proposed paper is a step in this direction.
2 Data and Methods
Using rvest package, we downloaded all available at that moment (11.2015) user
profiles from business social networking site MoiKrug.ru. In total there were
11000 profiles, containing more than 1000 unique skill tags. After preprocess-
ing was done: removing punctuation, making DocumentTermMatrix, correlation
matrix, we extracted the hierarchy of tags[6], using the co-occurrence of the tags
in profiles, and their network characteristics, built a hierarchical skill map.
Hierarchical clusterization was made, based on this distance matrix, to ana-
lyze skill areas underrepresented in the dataset. To analyze the quality of clus-
terization, we used silhouette[5] plot (Fig. 1), which shows the distribution of
observations in a cluster and their fit. Silhouette width calculates how close the
object is to other objects within the cluster in comparison with objects from the
other clusters, the higher it is, the better is alignment of the elements in the
cluster[0:1].
In addition, using an association rule learning algorithm Apriori agrawal:imieliski
and transactions between users and skills, we extracted frequent combination of
skills, characterising job profiles for the largest clusters.
3 Results
Analysis of the CVs tag hierarchy allowed us to identify two large skill clusters,
containing areas of general purpose Web development, High-load systems, and
Web- and UI- and graphics design, Project management, Internet marketing.
III
Clusterization based on the Jaccard index allowed us to extract 9 profes-
sional fields (Table 1), which we described as general web development, design,
backend, mobile application development, infrastructure, frontend, systems ad-
ministration, testing, administrative cluster.
Fig. 1. Distribution of observations in clusters
Further comparison of the clusters, using the dissimilarity metric, made it
possible to identify two large areas that are similar to the results of tag hierar-
chies, but in addition allowed to analyze smaller skill clusters.(Fig. 2).
Administrative cluster includes: marketing, analytics, sales, content and some
part of management. All of these areas are quite close to each other and less
presented in comparison with other sectors. Association rule analysis showed
that the most common combinations of skills in administrative cluster are: SMM,
Sales, Internet Marketing, Human Resource management, Project management.
Therefore, roles here are quite mixed. The most common skills in the cluster
of designers were UX-design, Adobe product family, Web design and Design of
mobile applications. Regarding the development of mobile applications, here we
have three main programming languages: Java, Objective-C and C++ and some
links to them from skills like: development for Android/ for IOS, XML, Qt, SVN.
In the sphere of backend the most common skills: Python and PHP. With Python
we usually can find: Django, Linux, PostgreSQL, while PHP is linked with Redis,
Laravel, MongoDB, Git, Symfony 2, Zend framework, Yii framework, Node.js.
MySQL is connected with both of them. In the area of software development,
we found trivial associations between HTML, CSS, Git, Javascript and JQuery
being prevalent. A more detailed study of the links between the clusters revealed
IV
Fig. 2. Hierarchical clusterization
V
that system administration, software development, and machine learning are
closely linked because of multipurpose programming language Python.
Unfortunately, algorithm did not reveal association rules for testing, frontend
and system administration because of the lack of data, CVs in these sectors.
Table 1. Professional fields and related skills
cluster skills n
Angular.js, JQuery, HTML, CSS, Node.js
1 25
Wordpress, Javascript, Web development, Git
Adobe Illustrator, UX design, Web design
2 15
UI design, Adobe Indesign, Graphic design
Python, Java, SQL, C#, Ruby, XML
3 68
MongoDB, MySQL, Yii framework, PHP
Swift, Development for iOS, Objective-
4 15
Unity3d, Development for Android, Jira, Shell
C, C++, Delphi, Linux, Microsoft SQL server
5 19
SVN, Unix, WCF, Wpf, Software Development
Grunt, Jade, Gulp bower, Less, Stylus
6 11
Adaptive layout, Cross-browser layout, Sass
System administration, Network Administration
7 9
Linux Administration, Project Management
Functional testing, Manual testing
8 12
Software Testing, Testing Websites
Sales, Internet marketing
9 24
Smm, Product Management
4 Conclusion and Future work
This paper presents the results of exploratory analysis of the Russian IT market,
based on the data from the business social network MoiKrug.ru. Skills clusters,
detected by the methods of social network analysis, revealed two large groups of
functional roles. Hierarchical clustering and association rules allowed us to form
nine clusters, which are closer to the professional fields. In addition there is an
idea of connectedness (common skills) and separateness of areas.
Although current results dont allow us to make a direct comparison with
the results of the previous studies of the IT market due the sampling bias, we
underline some contemporary trends, in particular – the mixing of roles and
an emergence of a large cluster of design jobs, interlinked with other IT areas,
compared with previous research.
While this sample is not representative to the whole Russian IT-industry with
a bias towards web-development and IT startup roles, and administrative sector
jobs being underrepresented, we still consider the results interesting as they allow
to discover flexible data-grounded job roles and skill patterns. Our current task is
to improve our skill matching approach to allow comparisons taking into account
VI
skills on different generalisation hierarchy levels and compare these results with
the structure, based on the job advertisements skills.
Acknowledgements
We would like to express our gratitude to Ekaterina Mekhnetsova, Stanislav
Pozdniakov, Daria Kharkina, Vadim Voskresenskii, Paul Okopny, and Viktor
Karepin.
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